Trong bối cảnh AI đang dần trở thành nền tảng cốt lõi của vận hành doanh nghiệp và quản trị quốc gia, các tin tức trong tuần không chỉ phản ánh xu hướng công nghệ, mà còn cho thấy cách các tổ chức đang chuẩn bị cho một kỷ nguyên vận hành dựa trên dữ liệu, tự động hóa và quyết định thông minh.

PHẦN 1: BẢN TIN TRONG NƯỚC

1. VIỆT NAM CHÍNH THỨC THÔNG QUA LUẬT TRÍ TUỆ NHÂN TẠO

Sự kiện nổi bật nhất trong tuần là Quốc hội Việt Nam chính thức thông qua Luật Trí tuệ nhân tạo, đánh dấu lần đầu tiên AI được điều chỉnh bằng một đạo luật chuyên biệt và toàn diện. Luật đặt ra các nguyên tắc phát triển và ứng dụng AI theo hướng lấy con người làm trung tâm, quản lý rủi ro theo mức độ tác động và đồng thời khuyến khích đổi mới sáng tạo.

Từ góc độ chuyển đổi số quốc gia, Luật AI không chỉ mang ý nghĩa quản lý, mà còn tạo hành lang pháp lý rõ ràng cho doanh nghiệp, startup và các tổ chức công – tư khi triển khai giải pháp AI trong vận hành, dịch vụ và quản trị. Những nội dung liên quan đến minh bạch thuật toán, trách nhiệm của nhà phát triển, thử nghiệm sandbox hay hỗ trợ hạ tầng tính toán cho thấy Việt Nam đang tiếp cận AI theo hướng “quản lý để phát triển”, thay vì kìm hãm đổi mới.

Đối với doanh nghiệp, đây là tín hiệu cho thấy AI sẽ không còn là xu hướng thử nghiệm, mà dần trở thành năng lực bắt buộc gắn với chiến lược chuyển đổi số bền vững và tuân thủ pháp lý.

2. TECHMART 2025: HƠN 100 CÔNG NGHỆ SỐ & AI PHỤC VỤ ĐÔ THỊ THÔNG MINH

Song song với khung pháp lý, Techmart 2025 mang đến góc nhìn rất thực tế về cách công nghệ AI và dữ liệu lớn đang được triển khai trong đời sống và quản lý đô thị. Sự kiện giới thiệu hơn 100 giải pháp công nghệ số từ các viện nghiên cứu, trường đại học và doanh nghiệp, tập trung vào các lĩnh vực như đô thị thông minh, y tế, giáo dục, du lịch và quản lý hạ tầng.

Điểm đáng chú ý là nhiều giải pháp không còn dừng ở mức trình diễn công nghệ, mà đã hướng đến khả năng triển khai và tích hợp thực tế, đặc biệt trong môi trường quản trị công và doanh nghiệp lớn. Điều này cho thấy chuyển đổi số tại Việt Nam đang bước sang giai đoạn ứng dụng sâu, đòi hỏi năng lực quản trị dữ liệu, chuẩn hóa quy trình và kết nối hệ thống.

Với doanh nghiệp, bài toán không còn là “có dùng AI hay không”, mà là dùng AI như thế nào để tối ưu vận hành, giảm rủi ro và tạo lợi thế cạnh tranh dài hạn.

3. HỢP TÁC ĐỔI MỚI SÁNG TẠO VIỆT NAM – NHẬT BẢN

Trong khuôn khổ các sự kiện công nghệ tuần qua, hoạt động thúc đẩy hợp tác đổi mới sáng tạo giữa Việt Nam và Nhật Bản tiếp tục được nhấn mạnh, đặc biệt trong lĩnh vực công nghệ, AI và startup. Các chương trình kết nối tập trung vào chuyển giao công nghệ, đầu tư, phát triển nguồn nhân lực và thương mại hóa giải pháp số.

Nhật Bản, với thế mạnh về công nghiệp, quản trị và tiêu chuẩn hóa, được kỳ vọng sẽ trở thành đối tác chiến lược giúp Việt Nam nâng cao chất lượng chuyển đổi số, từ mô hình quản trị doanh nghiệp đến phát triển sản phẩm công nghệ có khả năng mở rộng ra thị trường quốc tế.

Đây cũng là xu hướng chung của các quốc gia đang phát triển AI: không đi một mình, mà tận dụng hợp tác quốc tế để rút ngắn thời gian học hỏi và tăng tốc triển khai.

4. NỀN TẢNG DỮ LIỆU AI QUỐC GIA ĐƯỢC THÚC ĐẨY MẠNH

Một chủ đề xuyên suốt các hội thảo và tọa đàm trong tuần là vai trò của dữ liệu trong phát triển AI và chuyển đổi số. Các cơ quan quản lý và chuyên gia đều nhấn mạnh nhu cầu xây dựng nền tảng dữ liệu quy mô quốc gia, dữ liệu mở và cơ chế chia sẻ giữa các tổ chức.

AI chỉ thực sự phát huy hiệu quả khi được đặt trên hạ tầng dữ liệu chuẩn hóa, an toàn và có khả năng kết nối. Điều này đặt ra yêu cầu mới cho doanh nghiệp: không chỉ đầu tư công nghệ, mà còn phải chuẩn hóa quy trình, dữ liệu và hệ thống quản trị nội bộ – nền tảng để AI có thể tạo ra giá trị thực.

Đây cũng là lý do nhiều tổ chức đang tìm đến các giải pháp nền tảng số doanh nghiệp và quản trị vận hành thông minh để chuẩn bị cho giai đoạn AI hóa toàn diện, như các mô hình quản trị số, tự động hóa quy trình và ra quyết định dựa trên dữ liệu.

PHẦN 2: BẢN TIN QUỐC TẾ

1. ẤN ĐỘ THU HÚT HƠN 50 TỶ USD ĐẦU TƯ BIG TECH VÀO AI & HẠ TẦNG ĐÁM MÂY

Ở quy mô toàn cầu, Ấn Độ nổi lên như một trung tâm mới của chuyển đổi số, khi thu hút hơn 50 tỷ USD đầu tư từ các Big Tech vào AI và hạ tầng đám mây. Các khoản đầu tư tập trung vào trung tâm dữ liệu, nền tảng AI, đào tạo nhân lực và mở rộng dịch vụ số.

Điều này phản ánh xu hướng rõ ràng: AI không thể tách rời hạ tầng đám mây và dữ liệu quy mô lớn. Quốc gia nào xây dựng được hệ sinh thái hạ tầng – nhân lực – chính sách phù hợp sẽ có lợi thế cạnh tranh dài hạn trong kỷ nguyên số.

2. ĐÀI LOAN KHAI TRƯƠNG TRUNG TÂM ĐÁM MÂY NHẰM PHÁT TRIỂN “AI CHỦ QUYỀN”

Đài Loan chính thức khai trương trung tâm điện toán đám mây mới nhằm phát triển chiến lược “AI chủ quyền”, tập trung vào năng lực tính toán nội địa và bảo vệ dữ liệu chiến lược. Đây là một bước đi cho thấy AI đã trở thành vấn đề an ninh công nghệ và chủ quyền số, không chỉ đơn thuần là công cụ kinh doanh.

Xu hướng này dự báo sẽ lan rộng, buộc các quốc gia và doanh nghiệp lớn phải cân nhắc lại cách họ lưu trữ, xử lý và kiểm soát dữ liệu AI trong tương lai.

3. AI LEADERS WEEKLY: OPENAI, ANTHROPIC, META HỢP TÁC & CHUYỂN DỊCH CHIẾN LƯỢC

Một chuyển dịch quan trọng: các “ông lớn” AI như OpenAI, Anthropic và Meta đang mở rộng hợp tác, xây dựng tiêu chuẩn chung và tích hợp AI sâu hơn vào hệ sinh thái sản phẩm. Thay vì chỉ chạy đua mô hình lớn, trọng tâm đang chuyển sang khả năng ứng dụng, thương mại hóa và tích hợp vào quy trình doanh nghiệp.

Điều này củng cố xu hướng AI sẽ trở thành hạ tầng nền trong chuyển đổi số doanh nghiệp, tương tự cách cloud hay ERP đã từng đóng vai trò trung tâm trong các giai đoạn trước.

4. IBM ĐẨY MẠNH CHIẾN LƯỢC AI CHO DOANH NGHIỆP VỚI THƯƠNG VỤ CONFLUENT

Thương vụ IBM mua lại Confluent cho thấy một thông điệp rõ ràng: AI doanh nghiệp cần dữ liệu thời gian thực, chuẩn hóa và có khả năng mở rộng. Việc kết hợp nền tảng dữ liệu với AI giúp doanh nghiệp ra quyết định nhanh hơn, chính xác hơn và tự động hóa nhiều khâu vận hành.

Đây là minh chứng cho xu hướng AI không còn là “lớp công nghệ bên trên”, mà trở thành phần lõi của hệ thống quản trị doanh nghiệp.

KẾT LUẬN: KHI CHUYỂN ĐỔI SỐ BƯỚC SANG GIAI ĐOẠN “VẬN HÀNH THÔNG MINH”

Tuần 8/12 – 12/12 cho thấy chuyển đổi số đang bước sang một giai đoạn mới:

• Từ thử nghiệm sang chuẩn hóa.
•  Từ công nghệ sang quản trị.
•  Từ đơn lẻ sang hệ sinh thái dữ liệu – AI – vận hành.

Trong thời đại chuyển đổi số ngày càng sâu và phức tạp, doanh nghiệp không chỉ cần công nghệ, mà cần nền tảng quản trị đủ linh hoạt để tích hợp AI, dữ liệu và con người. Đó cũng là lý do các tổ chức đang tìm kiếm những giải pháp quản trị số, vận hành tinh gọn và tối ưu quy trình như nền tảng quản lý doanh nghiệp, hệ thống vận hành số, giải pháp quản trị thông minh… để sẵn sàng cho kỷ nguyên AI.

Thời đại chuyển đổi số không còn là câu chuyện của tương lai mà là bài toán vận hành của hiện tại.

Và đó cũng là lúc doanh nghiệp cần một nền tảng đủ vững để đi đường dài cùng AI.

The post OPUS WEEKLY – BẢN TIN CHUYỂN ĐỔI SỐ & CÔNG NGHỆ AI (8/12 – 12/12) appeared first on Opus Solution.

Hệ sinh thái Tính năng Đa dạng và Mạnh mẽ

Quản lý công việc và KPI (Task Management & KPI Monitoring)

• Theo dõi tiến độ công việc theo thời gian thực
• Giao nhiệm vụ, hỗ trợ trao đổi và thảo luận trực tiếp trên hệ thống
• Báo cáo tiến độ chi tiết theo phòng ban, nhân sự và mục tiêu công ty
• Nhắc nhở tự động theo SLA, tránh trễ deadline

Hệ thống yêu cầu và phê duyệt tự động

Tasken cung cấp loạt công cụ giúp doanh nghiệp quản lý quy trình phê duyệt, bao gồm:
Hệ thống yêu cầu phê duyệt (Approval System) – Tự động hóa quy trình duyệt tài liệu, hợp đồng, chi phí…
Quản lý đề nghị mua hàng (Request for Purchase Management) – Cho phép nhân viên tạo yêu cầu mua sắm, theo dõi phê duyệt và quản lý tiến độ đơn hàng.
Quản lý đề nghị thanh toán (Payment Request Management) – Hỗ trợ giám sát ngân sách, duyệt chi tự động, tích hợp hệ thống tài chính.

Quản lý tài liệu số (Document Management System)

• Tạo, lưu trữ và chia sẻ tài liệu nội bộ
• Hỗ trợ tìm kiếm theo từ khóa trong nội dung file
• Quản lý phiên bản tài liệu, chỉnh sửa đồng thời

Quản lý dự án trực tuyến (Online Project Management)

• Giám sát tiến độ dự án bằng Gantt Chart, Kanban Board

• Đồng bộ nhiệm vụ lên Outlook và thiết bị di động
• Bảo mật chặt chẽ, kiểm soát quyền truy cập

Quản lý nhân sự và chấm công

• Hệ thống đăng ký OT (Overtime Request System) – Nhân viên có thể gửi yêu cầu làm thêm giờ, quản lý duyệt và báo cáo số giờ làm thêm.
• Hệ thống quản lý nghỉ phép (Leave Request System) – Tích hợp với email để gửi thông báo duyệt đơn nghỉ phép.
• Chấm công và kiểm soát ra vào (Attendance & Access Control Tracking System) – Tích hợp với hệ thống chấm công tại nhà máy, văn phòng, giúp quản lý dữ liệu nhân sự dễ dàng.

Quản lý tài sản (Asset Management)

• Theo dõi tình trạng tài sản doanh nghiệp (máy móc, thiết bị, phần mềm,…)
• Hỗ trợ bảo trì định kỳ, lịch sử sử dụng
• Quản lý nhà cung cấp và các hoạt động liên quan đến tài sản

Hệ thống CRM – Quản lý khách hàng & bán hàng

• Quản lý tài khoản và liên hệ khách hàng
• Theo dõi pipeline bán hàng: Quản lý cơ hội, giao dịch, báo giá
• Tích hợp email và nhắc nhở tự động
• Báo cáo doanh số, phân tích dữ liệu khách hàng

Tích hợp chặt chẽ với Microsoft 365

Tasken hoạt động liền mạch với Outlook, OneDrive, Teams, SharePoint, Power Automate, giúp doanh nghiệp quản lý công việc dễ dàng mà không cần chuyển đổi giữa nhiều nền tảng.

Doanh nghiệp nào nên sử dụng Tasken?

Tasken phù hợp với mọi loại hình doanh nghiệp, từ doanh nghiệp vừa và nhỏ (SME) đến tập đoàn lớn, đặc biệt hữu ích cho:

• Các công ty có nhiều phòng ban, cần hệ thống phê duyệt rõ ràng
• Doanh nghiệp muốn số hóa quy trình làm việc
• Công ty muốn tối ưu hóa quản lý tài sản, tài liệu, nhân sự

Kết luận

Tasken không chỉ là một công cụ quản lý công việc mà còn là giải pháp văn phòng điện tử toàn diện, giúp doanh nghiệp vận hành hiệu quả hơn. Nếu bạn muốn trải nghiệm một hệ thống quản lý thông minh và mạnh mẽ, Tasken chính là lựa chọn tối ưu cho doanh nghiệp của bạn.

Để tìm hiểu thêm về các giải pháp số hóa doanh nghiệp hãy truy cập o365.vn hoặc liên hệ với Opus Solution để được tư vấn chi tiết. Chúng tôi sẵn sàng đồng hành cùng doanh nghiệp của bạn trên hành trình chuyển đổi số an toàn và hiệu quả.

The post Tasken – Giải pháp quản lý công việc tối ưu cho doanh nghiệp appeared first on Opus Solution.

Charts are a great visual tool for sharing information; they share your data’s story with a quick glance. For Power BI, charts are everything, and there are many from which to choose. One of the easiest to create is the sparkline visual.Power BI development Vietnam embeds tiny charts at the cell level in either the table or matrix visual. They’re easy to add and pack a lot of punch.

In this tutorial, I’ll show you how to add sparklines to a visual in Microsoft Power BI Desktop on a Windows 10 64-bit system. You can download the demo files for this Power BI tutorial.

What are sparklines?

Sparklines are tiny graphs that you embed into a data table. If you’re familiar with Microsoft Excel, you might have already encountered them. They’ve been around for a long time, and they’re popular because they fit into a single cell right next to the source data.

Viewers get a quick glance into trends, growth, minimum and maximum values, and so on. They’re easy to implement considering the story they tell about your data.

Choose sparklines when you want to display the details stored in a table with a chart that displays minimal information. Usually, Power BI hides the X-axis and given the space restrictions in a cell, you won’t use them for detailed analysis.

Before we create sparklines, we need data. In this example, we’ll import a simple Microsoft Excel sheet.

How to import Excel data into Power BI

We’ll import the simple Excel sheet shown in Figure A. If you’re following along using the demonstration .xlsx file, save it to OneDrive or SharePoint Intranet and note the location.

Figure A

Figure A: We’ll add sparklines to this simple sheet of data imported from an Excel .xlsx file.

To import this data, launch Power BI as you normally do, and close the information window. In the default window, click the Excel icon next to Get Data in the menu. In the resulting Open dialog, locate the demonstration file, PBIImportFromExcel_SourceFile.xlsx (Figure B), or the file you intend to use instead.

Figure B

Figure B: Open the Excel file.

Click Open to import the data into Power BI. The Excel file has a sheet and a Table object. In the resulting Navigator, check TableSales (Figure C) and then click Load.

Figure C

Figure C: Check the data source, TableSales.

With the data available in Power BI, let’s build a visual.

How to build the visual in Power BI

Power BI supports sparklines in the Table and Matrix visuals, which makes sense because Power BI embeds the tiny charts in cells.

To build a simple matrix visual, choose Matrix from the Visualizations pane. Populate the matrix by dragging Region and Date to the Rows bucket, and drag Amount to the Values bucket.

Figure D shows the resulting matrix, which requires only a bit of explanation.

Figure D

Figure D: The matrix groups the data by region and then date.

Expand any region in the matrix, and you’ll find the dates grouped for that region (Figure E). Click the plus sign to the left of a region to display the first subset, which is the year.

Figure E

Figure E: The dates are a group within the region group.

Thanks to Power BI’s time intelligence and the underlying Date table that Power BI creates automatically, you have several time components for filtering.

If the Date table is unfamiliar to you, consider reading How to know if the Auto date table is adequate when using Power BI, later. It isn’t necessary to complete this article, but it is something you’ll want to learn if you’re going to use Power BI.

Continue to click the plus signs to display all the time components for a particular region, so you can see how much information is automatically in your visual. I’m also working in Focus Mode, which makes the visual more readable.

Notice that I applied the currency format to the Amount column. You can do so in the Report window, but it’s much easier in the Data window. Using the Visualizations pane, it’s a few layers deep, and once you find the options, they’re a bit confusing.

Click the Data icon to the left. In the Data window, click the Amount header and then click the Currency icon ($) in the menu. Then, click the Report icon to return to the matrix visualization.

You’ve got a simple visual, so the next step is to add the sparklines.

How to add sparklines to the visual in Power BI

With a simple matrix visual ready, adding the sparklines may seem the easiest part. To do so, make sure to select the matrix if necessary, and in the Visualizations pane, find the Values bucket, and right-click Amount (Figure F).

Figure F

Figure F: Choose the Add Sparklines option.

In the resulting submenu, click Add A Sparkline. In the resulting dialog, choose Date as the x-Axis (Figure G). To do so, use the dropdown and Power BI will display the table’s hierarchy, so you can choose the right field.

Figure G

Figure G: Choose the date/time field for the sparklines.

Click Create to see the sparklines shown in Figure H.

Figure H

Figure H: The sparklines show sales over time.

If you’re wondering about the x-Axis choice, remember, sparklines are a time element. You’re viewing values over time. This option will always be a time/date column. The date values determine the time span.

The best place to learn more about that span is the raw data, which you can see by clicking the Data icon to the left. As you can see in Figure I, the dates begin with January 1, 2021, and continue through July 15, 2021.

Figure I

Figure I: The dates in the source data determine the time span.

As you expose more date information, as shown in Figure J, the sparklines lose their usefulness; they have no meaning when trying to represent single date values. For this reason, you might want to remove those layers at the bucket level, unless end users might be filtering down to the individual sales amount. However, if that’s the case, I suggest you create a second visualization for that.

Figure J

Figure J: Sparklines are useful only when they represent trends over time.

How to interpret the sparklines in Power BI

Sparklines expose information you might miss looking at a spreadsheet:

• The central region had a serious dip in sales and appears to have no sales for the end of the period. That calls for a closer look.
• Sales for the northeast region have declined over the period. Again, you will want to take a closer look at what’s happening.
• The northwest region had no sales at the beginning of the period but had a sudden surge at the end of the period.
• Southeast sales were trending up for a while but came back down.
• Southwest sales had a trend downward but appears to be recovering.

Stay tuned

We’ve successfully, and quite easily, added sparklines to a matrix visualization in Power BI. You could stop here, but stay tuned for a future article, in which I’ll show you how to improve the default results with a little formatting.

TechRepublic

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If you can’t make sense of your business data, you’re effectively flying blind. Insights hidden in your data are essential for optimizing business operations, finetuning your customer experience, and developing new products — or new lines of business, like predictive maintenance. Analytics is the means for discovering those insights, and doing it well requires the right tools for ingesting and preparing data, enriching and tagging it, building and sharing reports, and managing and protecting your data and insights. And as businesses contend with increasingly large amounts of data, the cloud is fast becoming the logical place where analytics work gets done.

For many enterprises, Microsoft Azure has become a central hub for analytics. Taking the broadest possible interpretation of data analytics, Azure solutions offers more than a dozen services — and that’s before you include Power BI, with its AI-powered analysis and new datamart option, or governance-oriented approaches such as Microsoft Purview. Leaving aside more specialized options such as ingesting telemetry, sharing data externally, or building machine learning models to deliver specific analyses, there are still enough Azure analytics services that you might wonder which one is best suited for any given job.

The truth is, Microsoft aims to provide CIOs a full stack of analytics services on Azure designed to work together, rather than a piecemeal approach, says Amir Netz, CTO of Microsoft Analytics. It is geared toward IT chiefs who want to be chief data officers, not chief integration officers, he suggests.

Although there is overlap between the various services, Netz explains that Azure’s analytics services broadly correspond to the layers an organization would build in creating an analytics architecture framework, “from creating the data lake and storing the data, processing the data in the lake and doing the data engineering, the ability to build data warehouses on top of that, to run machine learning algorithms, to do data science, to serve the data to business users,” he says.

Here we take a look at Microsoft Azure’s essential analytics services, what they are used for, and how they come together to make a comprehensive stack for your analytics strategy in the cloud.

1. Azure Analysis Services

If you’re used to using SQL Server Analysis Services for business intelligence, Analysis Services offers that enterprise-grade analytics engine as a cloud service that you can also connect to Power BI. But the features in Power BI Premium are now more powerful than the functionality in Azure Analysis Services, so while the service isn’t going away, Microsoft will offer an automated migration tool in the second half of this year for customers who want to move their data models into Power BI instead.

2. Azure Data Factory

Data Factory is a service for code-free data movement and data transformation pipelines to make it easier to integrate data from various sources into data warehouses: Think ETL (extract, transform, load) and ELT (extract, load, transform) as a service with built-in connectors, but with the emphasis on transforming and enriching data rather than just moving it into the right place (although you can also use it to move data into the cloud). Data Factory includes features such as “code by example” to help users build queries but also has options to use languages such as Python, Java, and .NET with Git and CI/CD support, making it particularly useful for migrating SQL Server Integration Services to Azure.

3. Azure Data Explorer

Data Explorer is a big data analytics platform that you can use, as the name suggests, for exploring data using KQL, also known as the Kusto Query Language, from the codename for the project which may or may not be a reference to exploring your ocean of data as if you were Jacques Cousteau. Azure Data Explorer is used to store and query data in services such as Microsoft Purview, Microsoft Defender for Endpoint, Microsoft Sentinel, and Log Analytics in Azure Monitor.

4. Azure Data Lake Analytics

Data warehouses are designed for questions you already know you want to ask about your data, again and again. Data lakes, on the other hand, enable you to store structured and unstructured data to explore with new questions that you haven’t asked before. Azure Data Lake Analytics helps you extract, clean, and prepare data from Azure Data Lake using R, Python, .NET, or U-SQL (which combines SQL and C#) to write queries, with key technologies from Azure Cognitive Services included as functions for processing text, speech, and images using machine learning. This is a serverless analytics job service that can handle petabyte scale data transformation, so you pay for the job rather than needing to manage infrastructure.

5. Azure Synapse Analytics

If you want to get away from building your own analytics framework that multiple teams in your organization will then use to extract data from data lakes and build data warehouses that business users have to access and work with separately, Synapse Analytics gives you the capabilities of cloud data warehouse and data lake services but lets you run your preferred analytic engine — whether that’s SQL or Spark — over all your data, structured and unstructured, without waiting for ETL processes or worrying about where the data is stored or how to connect to it. Synapse Analytics data flows are powered by Azure Data Factory, and if you use Cosmos DB, transactions in your operational database will be mirrored and available for analytics seconds after they’re recorded, so you can explore big data and relational data together. If the questions are useful enough to ask again and again, you can formalize them with traditional analytics techniques.

6. Azure Databricks

If you want to spin up Spark clusters on demand for transforming, cleaning, and enriching your data, Azure Databricks is an Apache Spark-based big data analytics service optimised for Azure with data adapters for various data types and an interactive workspace for building Spark dataflows. You can work in Python, Scala, R, Java, or SQL, but it’s particularly suitable for building AI systems and you can use common data science frameworks such as TensorFlow, PyTorch, and sci-kit learn, plus there’s integration with Azure Machine Learning.

7. Datamarts in Power BI

Think of datamarts as relational databases designed to do analytics at the business unit level rather than the enterprise data warehouse level, frequently driven by business users who need to collect data from multiple sources and integrate it together in a lightweight way. They don’t have the skills or the budget to provision a full relational data warehouse in the Azure portal, they don’t need the petabyte scale or even terabytes of data and they’re currently using technologies such as SharePoint lists or Excel for this, making it an underserved market with less governance than CIOs might prefer.

Datamarts in Power BI Premium are a fully managed, self-service, no-code option for up to 100GB of data with workloads automatically optimised for performance and a user interface that looks like Power Query (although advanced users can write DAX or SQL queries). Datamart discovers relationships between tables and generates the dataset, combining the semantic model of Power BI with the relational database model.

“You don’t need to know anything about how to be a DBA. We don’t ask you about partitioning scheme, or how to create an index,” Netz explains. “You don’t need to know how to write SQL and to import data or to query. Everything is visual. Everything is easy to use. Everything is designed for the user who knows how to create Power BI reports.”

Bonus: Azure Stream Analytics and Azure Time Series Insights

There are new ways to use analytics for which the cloud is particularly well-suited. Traditional analytics focuses on data in databases, but with sensors and IoT devices, you have transient, time-sensitive data that you want to process and take action on almost in real-time. The same is true for the clickstream form web and mobile apps. Azure Stream Analytics enables you to look at the data as it streams in and process it immediately to discover whether you want to take action. This processing wants to be done close to where the data is ingested, so you’d use Event Hubs to collect the data and pass it to Stream Analytics. You can also aggregate data to reduce the amount being stored and query it later to analyze trends or to forecast demand so that you’re storing, say, a moving average of the last second rather than recording the temperature every millisecond.

If you don’t want to build your own stack for that kind of analytics: Azure Time Series Insights is an end-to-end platform that takes data from IoT devices for you to monitor, analyze, visualize, and act on. You can use it to spot trends, highlight anomalies, and dig into root causes, and because Azure offers a full stack of analytics services, you can feed that data into other services such as Azure Databricks or use it to make models with Azure Machine Learning. But the market is moving away from single-purpose services and Time Series Insights is being deprecated in favour of Azure Data Explorer.

Besides Azure, Microsoft also have other solutions such as Microsoft 365, Power BI development Vietnam, Sharepoint that can help you improve your enterprise performance.

CIO

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However, as a Microsoft Gold partner, we found that Microsoft itself initially struggled to cope with the unexpected load and pushed customers toward a lean and simple solution — single, centralized tenants. Many CIOs quickly found that managing their large M365 tenant held unanticipated challenges. While having a single M365 tenant creates a uniform “known” environment for IT leaders, it also adds significant complexity and a lack of visibility. CIOs are essentially being asked to fly a plane without a cockpit.

The reason why enterprises struggle with this is simple: It wasn’t designed for them. The vast majority of Microsoft’s customers have historically been small businesses, so it’s developed its apps, reporting and service specifically for this market. It seems counterintuitive to think the largest enterprise software maker in the world caters its products to small businesses until you realize that small businesses account for 99.9% of all American companies. M365 is designed to scale to its customers’ needs but emphasizes end users’ productivity and speed over IT admins’ management and reporting. In fact, large businesses may experience throttling when using PowerShell or Graph API to build automation into their tenant.

This doesn’t mean enterprises shouldn’t use Microsoft Office365 Vietnam. It does, however, mean there are specific blind spots or challenges that CIOs must alleviate to reap the total value of their SaaS investment.

One large tenant creates insider threat potential.

With just a few hundred users, a single M365 administrator can handle the workload. However, as the tenant gets larger and employees become more distributed, the number of administrators grows and may even include a help desk. Add in Teams, focusing on productivity and collaboration between employees and external users, and now there are additional parts to the M365 tenant that also require IT and security teams’ attention. Enterprises scaling their SaaS environment often build out their IT team and provision it with global admin permissions to simplify operations. This is a mistake.

Forrester estimates (via Forbes) that 80% of data breaches are caused by compromised privileged credentials. Businesses with many global admins risk handing the “keys to the kingdom” to cybercriminals if any of them are ever compromised. Worse, in a single-tenant environment, businesses pass a lot of trust and responsibility to employees who may access sensitive information, conduct malicious activities (i.e., download financial information) or even make a mistake that could impact the business. Microsoft recommends at least two and no more than four global admins for any size tenant.

Whodunnit? Data Breach Forensics Required

In a world where data breaches are inevitable, data forensics are vital for businesses. Having access to up to a year’s worth of audit log data can greatly assist in determining when (and how) a breach occurred. Often, a bad actor will install software through a phishing attack and wait several weeks, or even months, before executing. Knowing when and how the software got installed empowers IT and security teams to plug security holes against future attacks. Forensic data also helps researchers determine where an attack originated and possibly by whom.

Microsoft offers tools that can temporarily store logs, but gaining insights from them can still take a significant amount of time. M365 logs are used in different services, so consolidation is critical.

Whose E5 license is it anyway?

License management is an underappreciated component of successfully managing a large M365 tenant. CIOs who don’t keep an eye on license costs and the health of their license pool — including active, inactive and underutilized licenses — could quickly find themselves diminishing their ROI.

Rightsizing an enterprise’s license pool is difficult for three reasons:

1. Each Microsoft app has its own API and data repository, so rightsizing licenses is extremely time-consuming and ever-changing because new services are added and removed, SKUs are updated and more.

2. Many organizations purchase E5 licenses for their employees — the “Cadillac” of M365 licenses. Their intentions are good; employees should have access to all of the apps and services they need to do their best work and leverage all possible security features. The reality, however, is that the vast majority of employees only need the M365 productivity suite (Word, Excel, Outlook), email (Exchange) and Teams, along with a limited set of security features related to their working environment. IT admins need to understand each department’s tech needs and assign the appropriate license to avoid extra charges for tools employees don’t need or use.

3. Employee onboarding complicates M365 license management as well. Many businesses purchase a new license when a new employee joins their firm. Simple, right? Unfortunately, the reality is that large enterprises have a steady stream of outgoing employees at any given time, so it may be possible to reassign an M365 license rather than buying a brand-new one. To understand if a license is active or not, enterprise IT leaders can aggregate signals from multiple services like Azure AD, Exchange, SharePoint Intranet, OneDrive, Teams and Power BI.

Buying additional licenses is easier, and nearly all enterprises go down this path. However, the problem is that you can’t remove unneeded licenses immediately as you can on Microsoft Azure. In many contracts, businesses must keep paying unused licenses until their annual license renewal.

Visibility is both the most valuable and most challenging thing to obtain within a large M365 tenant. With dozens of apps, services and reports available, CIOs must have a clear view of their users and critical data. Businesses that can’t gain insights into their cloud or hybrid infrastructure increase their risk, place undue admin on their IT teams and deplete the value of their SaaS investment, greatly reducing the promised ROI.

Forbes

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I asked some of the most innovative leaders at the SaaS companies we work with to share their best practices. Digital transformation is not just a strategy for this group – they live and breathe it every day. They were already executing at world-class levels before the pandemic hit, and they found an extra gear this year.

Here are five of their deceptively simple tips, and how you can use them to expedite your digital transformation journey.

Solve problems today, plan for tomorrow, repeat

This is a perfect mantra for any digital transformation team. But what does it mean?

Healthy technology organizations engage in continuous, sustainable change rather than expensive one-time projects that age all too quickly. Tim Hayes, director of cloud systems at DIGARC, explains: “This approach allowed our strategic transformation to address current challenges as part of a living project. One example of this approach involved running a parallel – and yes, we marked it as a strategic – initiative to build responsive [cloud] infrastructure capacity and also enabled support for continuous delivery. This enabled our engineering teams to more quickly deliver features to clients on DIGARC’s SaaS platform.”

As Hayes’s example highlights, strategic doesn’t always mean sexy. Sometimes the more exciting work of building features must co-exist with, or even take a back seat to, the necessary work of building the communication and technological frameworks that will enable your teams and technology to scale while you keep the lights on.

Build resilience and nimbleness through automation

Too often, we see organizations run out of steam (or money) before they are able to achieve all their goals. “Building sustainable profitability in today’s rapidly changing environment requires two things: resilience and nimbleness,” says Tamika McCuistion, CEO of Qore8. “Automation accomplishes both. Automation transforms a business process to its most frictionless form, allowing business to grow with the market.”

I couldn’t agree more. Many teams are so focused on rolling out product features to satisfy sales, the CEO, the board, or the customer that they don’t take the time to implement automation and DevOps. In fact, they may argue that implementing software development discipline will slow their teams or a projects down. That is a fallacy: the time spent upfront in automating what can be automated helps teams and deliverables flow more smoothly and efficiently.

It takes courage to push back to perceived higher business goals, but that is what a responsible digital transformation leader must do. The job of the remaining stakeholders is to fully understand and support these imperatives.

Another benefit of automation is that it frees up your smartest people to work on difficult problems rather than wasting their time on tedious ones. This creates a culture in which the right people and processes handle ever-changing environments, which is critical to speed.

Clearly define and evangelize your mission/vision to the organization

Teams and projects do not work in isolation; it is essential to get the entire company rowing in the same direction. Smart leaders work with their teams to clearly define and evangelize the organization’s mission and vision.

When the U.S. started shutting down in March following similar moves in Asia and Europe, educational software company ExamSoft saw a huge uptick in sales of its remote proctoring product, ExamMonitor. CEO Sebastian Vos said, “When we sat as a leadership team, we knew that our teams needed a clear set of guideposts to direct our decision-making. Armed with the why and where, the team created a compelling business strategy and transformation roadmap, which involved all teams across the company.”

With the sudden demand for their new product, Vos knew they needed organizational alignment around the company’s direction and strategy. Doing this work as soon as the crisis hit removed the stumbling blocks many transformational initiatives faced as the pandemic altered their plans. Instead of wasting time trying to figure out how each group would respond, everyone started on the same page.

This example serves as a good reminder to revisit your organization’s mission/vision consistently throughout your digital transformation journey – especially when you get to a crossroads.

Recognize and pay off technical debt

Too many teams fail because they are so keen to release a shiny new feature, adopt microservices, go to the cloud, or whatever the flavor of the day is. But some of the most heroic work in digital transformation is relatively mundane.

The need to scale is often taken as a clarion call to add more, but seasoned CTOs know that letting technical debt linger can make it expensive to maintain or scale complex systems.

Carl Ott, CTO of teleCalm, shares this example: “When sales started to climb and system load increased, we quickly identified several potential failure modes. Even with the pressure, we took the time to carefully trace these back to a handful of legacy shortcuts and design patterns. Fortunately, we were able to avoid mad-scramble emergency patches. We caught and addressed anti-pattern shortcuts early enough. We avoided customer-facing service disruption. And we added new capabilities at the same time.”

Every technology leader needs to understand that shortcuts can serve as a best practice – they can accelerate learning curves, reduce short-term waste, and speed up time-to-market – so don’t be afraid to take conscious shortcuts that lead to technical debt. But also budget time and resources to refactor and pay off technical debt as soon as possible.

See your people; measure your data

Finally, and very importantly, in this time of enormous stress, remember to slow down and recognize that digital transformation means nothing without the people who work on it and the people who will benefit from it. See your people. Talk to them and listen to them. Ask them what they need and give it to them (it’s only money or time). Slow down – so you can speed up.

At Synerzip, we do this by adhering to agile practices and by creating an environment of psychological safety for all our team members across all client projects. When things go wrong, we don’t look for blame – instead, we look for solutions and for what we can learn from the situation. After all, most problems result from a combination of factors rather than one individual, decision, or process.

And yes, measure your data. This sounds simple, but in practice, it can be challenging. Some teams don’t measure the right data, for example, or they measure too infrequently or inconsistently. Worse, the data measured might be used in a punitive way, eroding the psychological safety teams need to flourish and perform at their best.

The good news: You can iterate on this. There is no single correct answer to the question of what data you should measure to achieve your desired results. So just start measuring. Use an agile mindset, focus on process improvement, and continue to evolve until you find the combination that helps you and your team speed up your digital transformation.

The Enterprise Project

Our company also has experience in consulting technology solutions so that your company’s processes can operate effectively, helping to increase business productivity at a cost savings. If you want to learn more, do not hesitate to contact us – Opus Solution – a business consultant in Vietnam with many experiences accumulated through various projects.

Recently, we have just built and launched eoffice solution, Tasken, which integrates the most advanced features, not only to help you save costs and time, but also improve the productivity of your business.

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End of support means no new patches will arrive from Microsoft, including security patches. IT pros likely will need to replace or upgrade software before Microsoft’s deadlines to keep these SharePoint systems optimal.

The workflows component of SharePoint provides a selection of prebuilt applications in template form that can be used to automate various business processes, according to a Microsoft document description. Microsoft wants organizations to move to using Power Automate (formerly known as “Microsoft Flow”) instead of SharePoint workflows.

The newly announced deadlines for SharePoint workspaces only apply to organizations using SharePoint Online services, hosted by Microsoft. Organizations using SharePoint Server products “on premises” (or in their own datacenters) can continue to use SharePoint workspaces until 2026 if they have upgraded servers.

Here’s how Microsoft expressed that notion: “SharePoint 2010 and SharePoint 2013-based workflows will continue to be supported for on-premises SharePoint 2016 and SharePoint 2019 Server platforms until 2026.”

SharePoint 2010 Online Workflows Deadlines

Organizations using workflows with SharePoint 2010 Online have fewer than three months to react to Microsoft’s end-of-support announcement. Here are Microsoft’s deadlines on using SharePoint 2010 Online workflows:

• Starting August 1st, 2020, SharePoint 2010 workflows will be turned off for newly created tenants.  
• Starting November 1st, 2020, Microsoft will begin to remove the ability to run or create SharePoint 2010 workflows from existing tenants.

SharePoint 2013 Online Workflow Deprecation

The workflows component will continue to work with SharePoint 2013 Online, Microsoft’s announcement indicated, but it is still deprecating this component. Deprecation means that Microsoft isn’t planning to do further software development work on workflows, although security patches will continue to arrive.

Typically, a component might be expected to be supported until the end of the product’s support, but all bets are off for Microsoft’s online services. Microsoft’s announcement actually didn’t indicate how long workflows would be supported for SharePoint 2013 Online.

Microsoft is planning to turn off workflows for new SharePoint 2013 Online tenancies “starting in November.” However, organizations can still use it. They’ll need a certain PowerShell script to activate workflows, though, the announcement explained.

Workflows Use with SharePoint Designer 2013

Microsoft’s announcement also included a note about coming limitations of SharePoint Designer 2013 when it gets used with workflows on SharePoint 2010 Online.

Microsoft is planning to turn off workflows creation using SharePoint Designer 2013 for new SharePoint Online tenancies “starting August 2020.” It’s planning to turn off workflow creation using SharePoint Designer 2013 for existing SharePoint Online tenancies “starting November 2020.”

However, SharePoint Designer 2013 will continue to work with some SharePoint Server products throughout the server’s product lifecycle. Here’s how Microsoft expressed that notion:

SharePoint Designer 2013 will work with SharePoint Server 2019 for the remainder of the client support lifecycle (2026). SharePoint Designer 2013 will not be supported beyond that timeframe.

Power Automate Alternative

Microsoft contends that organizations using SharePoint Online workflows can make the switch to Power Automate and have the same functionality. However, comments left by readers of Microsoft’s announcement suggested that Power Automate isn’t up to speed in certain areas, such as enabling “automated permissions management of SharePoint content.”

Since SharePoint Online users already have Microsoft 365 licensing, they have the licensing to use Power Automate, which is part of the Power Platform suite of solutions, the announcement contended:

All Microsoft 365 licenses include usage of the Power Platform for customizing and extending Microsoft 365 applications. This includes both Power Automate and Power Apps.

While that notion may seem reassuring, Power Platform licensing is rather complex, so organizations may have to purchase “premium features” depending on how they use Power Automate. Office 365 licenses just give organizations the use rights to run custom Canvas-based apps with Power Automate, per a description in the document, “Microsoft Power Apps, Microsoft Power Automate and Microsoft Power Virtual Agents Licensing Guide”

Some Power Automate premium costs are associated with the use of certain prebuilt connectors to Azure or Dynamics 365 services, according to the document, which doesn’t show the costs.

Online Services and Degraded Support

Microsoft’s action to end support for workflows with SharePoint 2010 Online gives IT pros little time to react. They may still be recalling the product lifecycle support that was afforded with SharePoint Server on-premises products, although SharePoint Server 2010 is nearing its end on April 13, 2021. Microsoft actually had extended that server’s life by about six months, as described in an April announcement.

Microsoft can essentially end its Online Services products at will with its Modern lifecycle support policy. Its Fixed lifecycle support policies for servers still reflect the traditional 10-year cycle of support. However, Microsoft has suddenly revoked support there, too, as it did when it ended support for the Edge role component in Exchange Server 2016.

At some point, Online Services perhaps had its own lifecycle support cycle, perhaps tied the associated server product’s lifecycle. Consequently, IT pros could reasonably assume that a component of SharePoint 2010 Online would have the same support lifecycle of SharePoint Server 2010, namely until April 13, 2021. However, Microsoft seems to have folded its Online Services into its Modern lifecycle support policy at some point, which in effect offers no support assurances at all to organizations.

Redmondmag

For more information on these technology solutions, do not hesitate to contact Opus Solution. We are a business consultant in Vietnam with many years of experience, specializing in providing customers with Microsoft services such as Vietnam Office 365 solutions, Sharepoint Vietnam, Azure Vietnam, … and many others.

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Congratulations! After much hype and debate on whether to invest in digital transformation, you’ve successfully aligned leaders on strategy and priorities. You spearheaded an agile transformation to connect people with goals and empower them with the promise of great experiences and robust business solutions. And you embarked on a program to help your organization become more market-, customer-, and data-driven so that visions, priorities, and requirements adjust with evolving business conditions.

But what if you need to adjust your digital transformation plans? How do you recognize when you need to address new goals or obligations?

Ideally, CIOs should design transformation programs to embrace ongoing changes. Just like we want agile teams to adjust priorities based on customer feedback, CIOs must adjust digital transformation programs based on industry, market, strategic, and operational factors.

3 signals you need a digital transformation course change

But there are times when digital transformation programs require a pivot, reset, or realignment. Here are three situations that may signal the need for a course change.

1. Too many strategic priorities undermine execution

Digital transformation requires focus and discipline because any single initiative often drives significant transformative changes. True transformation initiatives impact all aspects of the business, from sales to marketing and operations. Pushing too many initiatives through the organization at once is a recipe for creating stress, under-delivering, or deploying with poor quality.

While initiatives should start small, they often grow in scope, complexity, and organizational reach to the point where they can overwhelm participants. Smart CIOs plan for this and project the timing of adding teams, maturing practices, and selecting technologies for transformation programs and prioritized initiatives.

It’s hard for leaders to prioritize and say no to strategic initiatives, so organizations often sign up for too many of them. But when you see high-potential leaders get stressed out, schedules fill with too many meetings, and agile teams miss deadlines, then it’s time to regroup with the senior leadership team. Present the facts and avoid walking away from the conversation without a clear statement that redefines a medium-term focus for the transformation program.

2. Business fundamentals have shifted

The clearest recent example of a shift in business fundamentals is COVID-19, which forced CIOs to implement business continuity plans, remote working at scale, and other operational changes. Even as governments open up businesses for restricted operations, there will be long-lasting impacts on supply chains, customer needs, sales, and operations.

CIOs must consider defensive and offensive changes to their transformation programs that align to new risks and opportunities. Seize this opportunity to drive positive changes in employee experience, hiring practices, and safety innovations.

Here are some other situations that may call for resetting and realigning digital transformations:

• Leadership changes
• Major acquisitions
• Brand crises
• Major shifts in market needs
• Significant regulatory changes
• Political turmoil

These fundamental shifts may require pivots in individual programs, realigning of priorities, or a full reset of the digital transformation. Under these circumstances, it’s critical that CIOs recognize the need for a pivot and take the lead in bringing the appropriate leaders together to discuss the required changes.

3. Your transformation strategy needs to align with breakthrough opportunities

You know a breakthrough opportunity when you see it. You recognize that a significant innovation must be shared and marketed to your business colleagues and may signify an even more significant achievement.

Let’s say your agile development team completes a pilot on a new mobile customer service application, and the response is overwhelmingly positive. Or a data science team, experimenting with a new reinforcement learning AI connected to thousands of sensors, demonstrates an innovation that can speed up manufacturing and lower costs.

CIOs need to realign people and other resources to take these innovations into production. But when there are breakthrough moments, leaders should use them to do more than just realign resources. CIOs should seek a “calling” – one key achievement that can make a lasting impact on the business – and seize that opportunity as the basis for transformation.

Transforming large businesses requires a culture change, and leaders should use breakthrough moments as tools to drive changes in how people collaborate and execute on new ideas.

Is that a pivot? Founders in Silicon Valley use the term to get their startups realigned to a new mission. If you have a breakthrough moment, I suggest pivoting the transformation program to maximize this opportunity.

Realign transformations and pivot only when required

While there are compelling business reasons to realign transformation programs, leaders must consider the organizational impact of major shifts in strategy. The staff will second-guess leadership that changes direction too frequently, and it also risks burning out key performers.

Some circumstances require a significant shift in direction, or pivot. Other times, it may be easier to communicate new priorities and small course changes. Keep in mind that the world is changing quickly, so there’s likely another speed bump or a big opportunity further down the road.

The Enterprise Project

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But as a recent experiment we conducted with medical coders demonstrates, emerging AI tools and techniques, coupled with careful attention to human factors, are opening new possibilities to train AI with small data and transform processes.

For every big data set (with one billion columns and rows) fueling an AI or advanced analytics initiative, a typical large organization may have a thousand small data sets that go unused. Examples abound: marketing surveys of new customer segments, meeting minutes, spreadsheets with less than 1,000 columns and rows. In our experiment, it was annotations added to medical charts by a team of medical coders — just tens of annotations on each of several thousands of charts.

Medical coders analyze individual patient charts and translate complex information about diagnoses, treatments, medications, and more into alphanumeric codes. These codes are submitted to billing systems and health insurers for payment and reimbursement and play a critical role in patient care.

Coders in our experiment, all of whom were registered nurses, were already accustomed to drawing on an AI system for assistance. The AI scanned charts and identified links between medical conditions and treatments and suggested the proper code for a given chart.

We wanted to see whether it was possible to transform the coders, responsible for the accurate, one-at-a-time assessment of charts, into AI trainers capable of enriching the AI with medical knowledge that would improve the system’s performance at identifying links.

What we learned over the course of the 12-week experiment is that creating and transforming work processes through a combination of small data and AI requires close attention to human factors. We believe that three human-centered principles that emerged from the experiment can help organizations get started on their own small data initiatives:

Balance machine learning with human domain expertise. A number of AI tools have been developed for training AI with small data. For example, few-shot learning teaches AIs to identify object categories (faces, cats, motorcycles) based on only one or a few examples instead of hundreds of thousands of images. In zero-shot learning, the AI is able to accurately predict the label for an image or object that was not present in the machine’s training data. In other words, it can correctly identify things it has never seen before. Transfer learning involves transferring knowledge gained from one task to the learning of new tasks — for example, identifying subtypes of cancer, based on knowledge of another type — which eliminates the machine’s need for a vast set of new data for performing the new task.

In our experiment, we employed a tool commonly called a knowledge graph, which explicitly represents the various relationships between different types of entities: “Drug A treats condition B,” “Treatment X alleviates symptom Y,” “Symptom Y is associated with condition B,” etc. It succinctly captures expert knowledge and makes that knowledge amenable to machine reasoning — for example, about the likelihood of a specific condition being present given the drugs and treatments prescribed.

To enable the coders to impart their knowledge to the AI, we developed an easy-to-use interface that allowed them to review contested links in the graph’s database. These were links where their colleagues, when reviewing individual charts, had disagreed with the AI — either by adding links unknown to the system, or by removing links it had added. Based on their expertise, the coders could directly validate, delete, or add links and provide a rationale for their decisions, which would later be visible to their coding colleagues. In addition, they were encouraged to follow their inclination to use Google (often with WebMD) to research drug-disease links, going beyond what they regarded as the existing AI’s slow look-up tool.

This combination of machine learning and human expertise has a significant multiplier effect. Instead of merely assessing single charts, coders added medical knowledge that affects all future charts. Further, with the AI taking on the bulk of the routine work, the need for screening of entire medical charts is greatly reduced, freeing coders to focus on particularly problematical cases. Meanwhile, data scientists are freed from the tedious, low-value work of cleansing, normalizing, and wrangling data.

Focus on the quality of human input, not the quantity of machine output. In the existing system, coders focused on the assessment of individual charts in high quantity. Over time, the AI learned from the accumulation of links added or rejected by a multitude of coders: Once a drug-disease link that the AI was not familiar with had been proposed a significant number of times by coders, a data scientist added it to the graph database. This manual process was undertaken only occasionally, in part because of the time lag in accumulating link proposals, and it relied on quantitative support for the link, rather than on medical expertise.

In the new system, coders were encouraged to focus less on volume of individual links and more on instructing the AI on how to handle a given drug-disease link in general, providing research when required. Links could now be considered for addition to the knowledge graph AI with a lesser burden of quantitative evidence. The AI would learn more regularly and dynamically, especially about rare, contested, or new drug-disease links.

Recognize the social dynamics in play on teams working with small data. In their new roles, the coders quickly came to see themselves not just as teachers of the AI, but as teachers of their fellow coders. Most importantly, they saw that their reputations with other members of the team would rest on their ability to provide solid rationales for their decisions. They spoke often of the importance of those rationales to the confidence of a subsequent coder encountering an unfamiliar link.

After only a few experimental sessions, a number of the participants asked that the number of characters in the tool’s rationale textbox be increased. Later, they asked that the research box be altered to accommodate more than one reference. Notably, they not only began to devote more time to each case than they had with the existing system, but to provide even more comprehensive rationales for their decisions as the experiment unfolded. Moreover, coders indicated they felt more satisfied and productive when executing the new tasks, using more of their knowledge, and acquiring new skills to help build their expertise. They also felt more positive about working with AI on a daily basis.

As small-data techniques advance, their increased efficiency, accuracy, and transparency will increasingly be put to work across industries and business functions. Think drug discovery, industrial image retrieval, the design of new consumer products, and the detection of defective factory machine parts, and much more.

But competitive advantage will come not from automation, but from the human factor. For example, as AI plays an increasingly bigger role in employee skills training, its ability to learn from smaller datasets will enable expert employees to embed their expertise in the training systems, continually improving them and efficiently transferring their skills to other workers. People who are not data scientists could be transformed into AI trainers, like our coders, enabling companies to apply and scale the vast reserves of untapped expertise unique to their organizations. Further, the results that emerge from small-data applications will come not from a black box, as they do in data-hungry applications, but from human-machine collaboration that renders those results explainable and therefore more trustworthy both inside and outside the organization.

Mastering the human dimensions of marrying small data and AI could help make the competitive difference for many organizations, especially those finding themselves in a big-data arms race they’re unlikely to win.

Harvard Business Review

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There a few reasons why the Coronavirus, or COVID-19 have forced companies to visit a digital transformation faster. One Forbes contributor Andrew Filev in his column “COVID-19 Is A Before and After Moment In The Digital Transformation” sees the most drastic change in four areas 1. telecommuting 2. on-demand food and services 3. virtual events 4. the cloud.

Telecommuting

Many people in industries that formerly prohibited are now working from home, from bankers, to engineers in aerospace, to almost every teacher in America, work-life has changed for most of us. In order to not only continue working, we are spending our social time on video calls as well, in order to not completely isolate.

On-Demand Food and Services

Grocery delivery is now the norm for many people who wouldn’t bother in the past. My own brother who lives in NYC ordered groceries from Amazon and said he could only get a delivery from 5am to 7am, but was frustrated when they arrived at 4:50am. At my own Whole Foods Market in Oakland on a recent Friday said they were “sold out” of delivery. Insurance companies who made it difficult for patients to be reimbursed for telehealth or remote services, will now need to change their tune and reimburse for things like remote therapy. Today if you can’t alter the way your products and services are delivered, you are dead in the water, as most of America and the world are under strict shelter-in-place orders.

Virtual Events

With no one flying, and gatherings prohibited, the events industry took a big hit this Spring. But many companies have simply shifted their budgets to digital events or digital content. Only time will tell if the fall will be a very busy events season, or companies will decide they look doing more online events over in-person events, and we’ll see more innovation on the virtual events side. From an internal corporate perspective, every day I see people posting photos of their large online meetings with their coworkers, and fun stories from teams that are learning to enjoy this new way of working.

The Cloud

Without the cloud during the Coronavirus pandemic, companies would struggle to share and co-edit documents securely, access analytics and much more. Even short physical distances would present a challenge for collaboration between coworkers without the cloud. Real-time wouldn’t be as easy, streaming would be a problem, smart phones wouldn’t be smart, and rapid data to name a few.

For many of you that are not used to a digital way of work, COVID-19 might have expedited your timeline for a digital transformation – and that’s not a bad thing.

Digital transformation seems to be the current business buzzword. But not all digital transformations are created equally.

According to research in Harvard Business Review, of the $1.3 trillion spent on digital transformation in 2018, an estimated $900 billion was wasted when initiatives didn’t meet their goals. You don’t want this to be your company.

Although most companies understand the importance of digital transformation, many are overwhelmed by the idea of having to revamp their entire digital approach and flounder without knowing how to implement a transformation. But they also realize that if they don’t do anything, they run the risk of being disrupted and replaced.

The goal of a digital transformation is to use technology to solve traditional problems, which means integrating technology into every area of the business. When done right, digital transformation allows companies to provide unprecedented value to customers.

Companies start a digital transformation, but it’s never truly over. A true digital transformation is a state of mind for a company to continually evolve and adopt new digital solutions internally and externally. One of the first goals of digital transformation is to break down internal silos to create a seamless internal experience. When a company works well internally, it greatly affects the external customer experience. Every area of the company has a role to play in digital transformation, and they each impact the customer in unique ways. Lasting digital transformations are customer-focused with an eye towards the future.

Digital transformation doesn’t have to be overwhelming. It’s not something to check off a list, but instead a mindset that becomes part of the organization’s culture and experience. When a company approaches a transformation with that in mind, it creates a much more manageable transformation. Many of you are now working against the clock, and my 12 steps might get you there faster.

Here are my 12 steps to digital transformation, adapted from my book The Customer Of The Future:

1. Customer focus. Before a digital transformation can truly begin, the company must switch its mindset from being product-focused to being customer-focused. The driving force behind technology decisions should be customers, and the goal should be to make their lives easier instead of making things easier for the organization. A customer focus is the basis for all other digital transformation decisions.
2. Organizational structure. Companies need to break down internal silos to create a cohesive organization that embraces change. That means getting executives and leaders on board with the new digital vision.
3. Change management. Change is hard, no matter how much it will benefit the company. One of the most common reasons digital transformations fail is because employees don’t support them. The most effective change management efforts are aligned with the modern, dynamic business environment.
4. Transformational leadership. A successful digital transformation starts from the top with leaders who drive employees towards the vision. Every executive and leader must play a role in championing digital change and uniting the digital transformation with the company’s larger, long-term goals.
5. Technology decisions. Digital transformation impacts the entire organization, not just one department. An average of 15 people are involved in most technology purchase decisions, which means that everyone’s voices need to be heard.
6. Integration. All data systems need to work together and be integrated into the company’s internal processes. A streamlined data strategy is required for a successful digital transformation.
7. Internal customer experience. When focusing on digital solutions for customers, companies also need to consider their internal customers—employees. Getting employee feedback and providing consumer-grade technology solutions empowers employees to provide an amazing experience.
8. Logistics and supply chain. Digital transformation can be powerful in improving the speed and reliability of the supply chain, from how fast products are manufactured to the speed and efficiency of order fulfilment and delivery. To fully leverage a transformation, companies need to look at how the supply chain can be digitized and improved.
9. Data security, privacy and ethics. Adopting new digital solutions opens the doors to new questions about data security. Most consumers think their personal data is at risk, which means that adopting company-wide standards for privacy and security should be top of mind. With the many examples of “Zoom Bombing” last week, with hackers sneaking into people’s private meetings — again we are reminded that any data breach or hack can erode your brand. School districts are now looking to alternatives to Zoom.
10. Evolution of products, services and processes. Digital transformation requires a change in thinking about how an organization delivers its products and services, and even the products and services themselves. Successful companies push past what’s always been done to find the most efficient and innovative solutions.
11. Digitization. Digital transformation touches all areas of the organization and blurs the line between digital and physical stores. That means moving past segmented operations to digitize every aspect of the business.
12. Personalization. Digital transformation provides unparalleled opportunities to offer personalized service to customers. Leverage digital solutions to understand customers and provide recommendations and experiences that are unique to them.

Digital transformation is an ongoing process, which means continually working through these 12 steps. Change and venturing into the unknown can be difficult, but the benefits that come from creating a forward-thinking, customer-focused, digital company can be lasting. Perhaps this is the silver lining for businesses in the age of COVID-19, forced change can be positive – for your employees, partners and customers.

Forbes

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