In the industrial manufacturing world, this has certainly proven true. Here, digital transformation has always been about automating manual tasks, enabling access to information, using analytics to drive decision-making and upskilling the workforce — all powerful tools to enable remote work.

Let’s look at some examples.

Replacing Manual Readings And Inspections With Sensors

A new generation of easy-to-install wireless sensors has made it inexcusable to continue sending employees into installations to gather production data or inspect equipment and potentially expose them to dangerous situations. This includes process variables such as levels or flow rates, as well as safety and equipment health variables — vibration, temperature, acoustic, etc. For example, manufacturers now have access to advanced wireless corrosion sensors, eliminating manual rounds with hand-held instruments. In addition to the initial immediate benefit, data from those sensors is continuously available to power more sophisticated analytics applications. And, if desired, the data can be securely monitored by outside experts in a connected service type model, allowing for complete outsourcing.

For example, Chevron added sensors to its heat exchangers to collect real-time data about the equipment’s health and predict future conditions. Chevron is also piloting the use of sensors to measure pipe corrosion and tank levels at its facilities.

“Digital Twin” Models Replace the Real Thing

The use of sophisticated digital models, or “digital twins,” enables virtual interactions that replace their physical counterparts. Sophisticated automation systems, including purpose-built hardware, used to be physically set up entirely for engineers to configure and customize. Manufacturers would then need to travel to perform configuration and tests to ensure the automation systems were set up properly. Now, those facility-specific configurations are hosted in the cloud; engineers from anywhere in the world perform programming and configuration; and engineers and manufacturers together test the system from wherever is convenient.

Furthermore, simulations (digital twins) of the actual process can be connected to the automation system, enabling operators to train on a completely virtual process. Even a facility’s physical layout can be modeled using a 3-D virtual reality simulation, eliminating travel to the facility for field procedure training. Customers who were initially hesitant about these new practices are now grateful projects can continue with their employees in the comfort and safety of their homes.

One leading pharmaceutical company is using cloud-hosted digital twin technology to fast-track the design, engineering, testing and validation of a facility expanding for COVID-19 drug production. Digital twins will also help the pharma industry predict the future of assets and provide better insights on product performance.

Another common use of the digital twin is reliability. Existing sensor data augmented with new equipment health sensors feed online models of equipment’s physical behavior. Those models provide early warning of failures and, using a principle called failure mode and effects analysis (FMEA), can diagnose the root cause of impending failure, predict remaining useful life and prescribe repairs. Unplanned downtime is prevented, and routine maintenance is eliminated. And when a potential failure is detected, repair personnel go to the field prepared with the equipment, training and parts for the task at hand, limiting their time on-site.

Problem Solving Digitally Across Multiple Disciplines

The most immediate problems in a manufacturing facility require multiple disciplines to coordinate a response and determine implications for health, safety and the environment, impacts to production, availability of spare parts and skilled personnel, work permits and more. Fortunately, a new generation of digital collaboration tools, capitalizing on new sensor data and technologies like augmented reality (AR), have enabled collaboration to occur literally anywhere.

First, these applications alert maintenance personnel to equipment that needs attention and provides diagnostic displays with available information. Using mobile devices, field repair personnel then virtually access SMEs inside or outside the company to share what they are seeing and hearing, receive help via AR tools, access real-time data and documents, and collaborate with other disciplines. We may still need at least one human to accomplish the physical repair task, but that human is enabled in a way they’ve never been before. Our valve experts in Houston, for example, recently used remote assistance with AR technology to help a European power company evaluate and repair a valve issue, saving tens of thousands of dollars.

To get started on your digital transformation journey, start with your business consultant goal in mind. It’s not about the latest and greatest technology; it’s about looking at known problems and putting technology to work to focus on them. Enabling a remote workforce has become an immediate priority for many, but it’s not the only business challenge that digital transformation can tackle. Is it sustainability? Worker safety? Optimizing production to market demands? Knowledge drain from a retiring workforce? These are all problems digital transformation technology can help.

So, go ahead and put your data to work. It’s one thing to have a lot of data. It’s another to get the right data to the right people to make informed decisions — and the people aspect of digital transformation is often overlooked. Dedicate time and resources to engage and empower your workforce as new technologies and processes change longstanding practices. Once you’ve demonstrated success, think bigger and scale proven success across your facility or enterprise.

Recent events have certainly put these technologies to the test — but worker health and safety, and the digital transformation tools that help protect employees, have always been and always will be a top priority for manufacturers.

Forbes

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The COVID-19 pandemic has had a devastating impact on our world, affecting our day-to-day lives in ways we never could have imagined even a few short months ago.

For many organizations, these changes have led to a fundamental shift in how we define business as usual, causing many companies to reassess and reprioritize their business and IT goals and budgets in dramatic and unexpected ways.

During this environment of unprecedented change and uncertainty, it may be tempting for some companies to put digital transformation initiatives on hold and instead focus on more familiar and less disruptive efforts. But this is already a time of disruption, and many aspects of digital transformation are exactly what’s needed to help your organization move forward, whether you seek to deliver more value to customers, pursue new opportunities for growth, improve efficiency—or all of the above.

As you contemplate what’s next for your organization and industry, consider how the following four IT initiatives can help advance your digital transformation goals and better position your organization to grow and succeed today—and throughout all the better days ahead.

Break Through Silos With Agile Practices

With workers physically separated, many are seeking clever ways to encourage collaboration, teamwork, and community. From digital water cooler chat hours to new platforms and technologies, the current remote work scenario has in many instances led to new connections with departments or individuals collaborating that have never worked together before.

As these connections are forged, you have a unique opportunity to encourage DevOps style practices throughout IT operations. Self-service help desks, team integrations, and feedback loops that span traditionally siloed arenas such as ITOps, development, security, and support are some of the ways you can foster innovation by dissolving departmental barriers. Gaining C-level buy-in is a critical success factor to rolling out agile initiatives with clear and regular communication about who is involved, what needs to happen, and why these changes make sense given company goals.

Make “Everything as a Service” a Reality

The industry has been kicking around the term “XaaS” for years as marketing teams added “aaS” to the end of just about any kind of web-based, on-demand service. Now more than ever, the time for XaaS is here. One example is large scale events going digital-only with “Events as a Service.”

The current environment is ideal for scaling a cloud and -as-a-Service strategy because it allows companies to be more agile amid fluctuating market conditions. As monthly consumption for IT services grows, companies will need IT staff to manage subscription spend. As leadership grows more comfortable with operational expenses versus fixed cost spending on physical hardware, it can lay out a plan to continue scaling services into the future.

The key is determining which platforms and applications are a good fit to change to a consumption-based model, as going wholesale into the cloud may lead to greater costs for some situations.

Focus on Customer Experience

Despite the devastation caused by the pandemic environment, it has also brought out the best in many of us. More than ever, individuals and organizations are looking out for our neighbors, families, small businesses, and communities at large. And although it’s easy to dismiss pandemic-oriented services as opportunistic, the general attitude of customer-centricity is one many companies are looking to adopt moving forward.

The customer experience is what ultimately drives revenue or proves the value of IT services (and the individuals providing them). One of the first steps to digital transformation is changing the driving force from products to the customer experience.

When considering this move, ask: “How does our technology make things easier for the customer or the end-user?” Soliciting and implementing feedback from users into technology delivery, and doing so quickly enough so that they take notice, will lead to amazing experiences that help your organization grow and retain customers.

Identify Opportunities to Automate

If you’ve put certain projects on hold, consider transitioning that IT staff to other initiatives aimed at gaining efficiencies and reducing cost through automation.

Consider experimenting by automating simple and routine tasks using mature technology like business rules engines, mobile app platforms, and native cloud automation tools. Companies that are ready to go deeper can explore low-code, AI, and machine learning.

Many other changes to IT operations may occur, either as a direct result of the pandemic or as a related side effect. But the use of automation, agile practices, customer-centric services, and “-a-a-S” and cloud solutions will play a role in other technologies and processes the company implements.

While it can be overwhelming to throw too much change at your organization at once, it’s worthwhile to use this time to prioritize your transformation efforts and map out a plan. So when we do get through this difficult time, we’re all prepared and better positioned to move forward together.

TechNative

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Success requires bringing together and coordinating a far greater range of effort than most leaders appreciate. A poor showing in any one of four inter-related domains — technology, data, process, or organizational change capability — can scuttle an otherwise well-conceived transformation. The really important stuff, from creating and communicating a compelling vision, to crafting a plan and adjusting it on the fly, to slogging through the details, is all about people.

More than anything else, digital transformation requires talent. Indeed, assembling the right team of technology, data, and process people who can work together — with a strong leader who can bring about change — may be the single most important step that a company contemplating digital transformation can take. Of course, even the best talent does not guarantee success. But a lack of it almost guarantees failure.

Let’s explore the talent needed in each of the four domains in turn.

Technology

From the Internet of Things, to blockchain, to data lakes, to artificial intelligence, the raw potential of emerging technologies is staggering. And while many of these are becoming easier to use, understanding how any particular technology contributes to transformational opportunity, adapting that technology to the specific needs of the business, and integrating it with existing systems is extremely complex. Complicating matters, most companies have enormous technical debt — embedded legacy technologies that are difficult to change. You can only resolve these issues with people who have technological depth and breadth, and the ability to work hand-in-hand with the business.

Challenging as these difficulties are, an even more critical issue is that many business people have lost faith in their IT department’s ability to drive major change, as many IT functions are primarily focused on “keeping the lights on.” Eventually, however, digital transformation must incorporate institutional IT, so rebuilding trust is essential. This means that technologists must provide, and demonstrate, business value with every technology innovation. Thus, leaders of the technology domain must be great communicators, and they must have the strategic sense to make technological choices that balance innovation and dealing with technical debt.

Data

The unfortunate reality is that at many companies today most data is not up to basic standards, and the rigors of transformation require much better data quality and analytics. Transformation almost certainly involves understanding new types of unstructured data (e.g., a driver-supplied picture of damage to a car), massive quantities of data external to your company, leveraging proprietary data, and integrating everything together, all while shedding enormous quantities of data that have never been (and never will be) used. Data presents an interesting paradox: Most companies know data is important and they know quality is bad, yet they waste enormous resources by failing to put the proper roles and responsibilities in place. They often blame their IT functions for all these failures.

As with technology, you need talent with both great breadth and depth in data. Even more important is the ability to convince large numbers of people at the front lines of organizations to take on new roles as data customers and data creators. This means thinking through and communicating the data they need now and the data they’ll need after transformation. It also means helping front-line workers to improve their own work processes and tasks such that they create data correctly.

Process

Transformation requires an end-to-end mindset, a rethinking of ways to meet customer needs, seamless connection of work activities, and the ability to manage across silos going forward. A process orientation is a natural fit with these needs. But many have found process management — horizontally, across silos, and focused on customers — difficult to reconcile with traditional hierarchical thinking. As a result, this powerful concept has languished. Without it, transformation is reduced to a series of incremental improvements — important and helpful, but not truly transformative.

In building talent in this domain look for the ability to “herd cats” — aligning silos in the direction of the customer to improve existing processes and design new ones, and a strategic sense to know when incremental process improvement is sufficient and when radical process reengineering is necessary.

Organizational Change Capability

In this domain we include leadership, teamwork, courage, emotional intelligence, and other elements of change management. Fortunately, much has been written about this domain for many years, so we won’t review it here, other than to note that anyone responsible for digital transformation must be well-versed in the area. While, we have no firm evidence to support this, it seems that those who gravitate toward technology, data, and process are somewhat less likely to embrace the human side of change. Of course, in our recommendations above, we have urged leaders to seek those with excellent people skills. If you are unable to find them, a good alternative is to put some “purple people,” those able to work on both sides, on the transformation team.

Pulling It All Together

So far, we’ve discussed the technology, data, process, and organizational change capability domains as if they existed in isolation, which of course they don’t. Rather, they are part of a larger whole. Technology is the engine of digital transformation, data is the fuel, process is the guidance system, and organizational change capability is the landing gear. You need them all, and they must function well together.

Consider the “our systems don’t talk” problem, which bedevils most companies and is anathema to digital transformation. But in which domain does it belong? As described above, it is a tech problem — but it also leads to enormous process inefficiencies. Yet it stems from a lack of solid data architecture, and it may involve organizational structure and politics issues that are difficult to change. So one could argue that any domain should take the lead. But the best solution involves the four working together.

Absent a deep understanding of each domain, it is difficult for nearly all business leaders to see the full potential in digital transformation — a contributing factor to many failed digital transformations. But of course, no one individual possesses all the required knowledge and capability. Hence our call to assemble talent in each area.

Finally, work on technology, data, and process must proceed in an appropriate sequence. It is generally accepted that there is no sense automating a process that doesn’t work, so in many cases, process improvement or reengineering must come first. On the other hand, some transformations will feature large doses of artificial intelligence. Since bad data stymies development and deployment of good AI models, in these cases, work on data should come first. Start with your end goals, then develop the sequence of steps best suited to achieving them

Digital transformation can and should be focused on problems of greatest need to the company. Those priorities will also lend a flavor to the talent needed; if the focus is on transforming customer relationships, for example, the data talent on the team may have particular expertise in customer data, the process talent on sales and marketing processes, and so forth. More important, however, is that the talent possesses the four types of expertise we have described and has had previous success at creating and executing on any kind of technology-driven transformation.

Harvard Business Review

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The Redmond, Wash., tech giant, revealed the update, which includes over 20 new features, on May 6 during its virtually held Business Applications Summit. Eight of the new features are now generally available, while five will be available later this month and 10 later in 2020, Microsoft said.

The Power BI update, according to a Microsoft blog post, attempts to enable the development of a data-driven culture by adding more augmented intelligence capabilities to the vendor’s analytics platform, helping organizations scale their business intelligence operations within one platform through data lineage and other capabilities, and weaving BI into the fabric of the organization by embedding analytics features into tools like Teams and Excel.

Given the scale of the Power BI update and its existing popularity, analysts said the new features add significant vitality to the platform.

“All of these announcements are important steps that Microsoft is taking to further cement themselves as leaders in this space,” said Mike Leone, senior analyst at Enterprise Strategy Group. “They are not resting on their laurels. They’re further empowering all users to effectively bring data to their jobs on their terms.”

Doug Henschen, principal analyst at Constellation Research, also said the Power BI update adds notable new capabilities to an already widely used platform.

“Microsoft has delivered a truly impressive collection of updates, and the scale of the Power BI community continues to grow,” he said.

Henschen cautioned, however, that many of the new features are designed to work only with other Microsoft tools.

“The announcements in total struck me as a bit insular and aimed at a Microsoft-only world,” he said. “For example, I very much like the theme of embedding Power BI insights where decisions are made — which is a direction all BI and analytics vendors need to go — but all of the embedding options presented were Microsoft products and services, from Power Apps/Power Automate, to Office, Excel and Teams. I’d like to hear at least a nod to how Power BI works in and is open to a heterogeneous world.”

Key new features that add AI capabilities include smart narratives, a natural language generation (NLG) tool that will allow report authors to add interactive narratives that are automatically generated by Power BI and give end users explanations about their constantly updated data, and a new mobile report authoring capability that will give report authors new tools to create mobile versions of their existing reports.

Features aimed at helping organizations scale their business include new enterprise semantic modeling capabilities such as shared and certified data sets that help users build reports based on trusted data and data lineage analysis to enable users to understand where their data came from and how it’s been modeled through its lifecycle.

And lastly, among the features designed to weave BI into the fabric of an organization is PowerPoint for Data, which provides prebuilt templates for building and laying out report pages; the ability to connect to Power BI data sets without leaving Excel; and the addition of Power BI tab and preview links to Teams.

Perhaps the two features that stood out most to the analysts were the new NLG and data modeling capabilities.

“I recognize it’s a sneak peak, but smart narratives sounds fantastic,” Leone said. “I’m viewing this as a doubling down of sorts to AI explainability. By taking a data champion, having them narrate their findings and rationale, and then handing them over to the masses is an incredibly powerful way to spur creativity and ideas on ways to interact with the data.”

Henschen similarly cited the improved NLG prowess included in the Power BI update as a key new feature. Regarding the new data modeling features, he noted that Microsoft is addressing a problem that arose with the advent of self-service analytics. With multiple users within an organization modeling and analyzing the same data at their own workstation, there were multiple interpretations of the data.

The Power BI update — which came as competitor Tableau also updated its platform last week — aims to better standardize the modeling process.

“With [last] week’s announcements, they’re raising the bar to promote consistency and reuse,” Henschen said. “Power BI’s new Composite model capability will enable users to start with data that is already modeled without crimping their freedom to explore and add new data. They’re extending that model in a way that will promote productivity and reuse without breaking anything.”

Leone, meanwhile, said that the data modeling capabilities address trust. By making the models more consistent, they promote a level of trust in the underlying data that didn’t previously exist.

“If you lack trust in the underlying data, the outcomes don’t matter,” he said. “To further drive usage of BI tools, vendors must ensure trust, and … the announcements are about ensuring the highest level of trust and visibility into the who, what, where, when, why, and how of data.”

Search Business Analytics

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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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This should be easier to do today. Technology and business intelligence (BI) provide a wealth of data to guide even the most nuanced decision-making. For many scenarios, there is data that can show you the outcome of past decisions that were similar and reveal the projected outcomes over time.

Despite this, many leaders aren’t taking full advantage of the tools at their disposal and rely heavily on gut-instinct in situations where data provides a more complete picture. In situations without data or precedent, instinctive decision-making is likely the most viable option. But this strategy is unnecessarily risky in cases where the data shows the outcome of similar situations that have occurred in the past.

In these cases, many leaders use past exceptions as justification to ignore the cost of failure. Reasons for this may vary — from a distrust of analytics to a desire to succeed with a bold, unconventional move — but it can prove costly in the long-run.

An illustration of this is professional gambling. Casinos thrive because many bettors believe they are smarter than the odds, and that they can beat the house with bold betting. These are the gamblers who drive the majority of casinos’ profits.

The bettors who win in the long-run clinically assess the odds of each bet and make careful, data-backed decisions, making their biggest wagers when the odds are in their favor.

Statistics tend to normalize over time, eliminating the short-term aberrations that give the false appearance of good or bad luck. The longer you play the same game, the more the odds win out.

The Flashbulb Memory Problem

When relying on prior experience, consider that memory is inconsistent and fallible. We are more likely to recall extremely unexpected events, rather than more mundane occurrences, thanks to “flashbulb memory.”

According to the American Psychological Association, flashbulb memory describes distinct recollections of emotionally significant occurrences. APA notes, “Though flashbulb memories are more likely to be retained than the memory of an everyday event, they are not always accurate.”

In a business context, flashbulb memory causes people to remember exceptional results, rather than expected outcomes. For example, an executive may vividly remember taking a chance on an unconventional hire and watching that employee grow into a star performer. They are less likely to remember when they made a safer bet on an obviously qualified candidate who turned out to be exactly as competent as expected, or the risky hires that did not work out. The exception becomes the legend.

Taking Advantage of Data

There’s a huge difference between understanding the importance of data and making it a priority in your organization. Every business needs experts responsible for analyzing pertinent data and helping inform employee decision-making.

For example, at Acceleration Partners (AP), a member of our team is responsible for using BI to tell us which brands, based upon their attributes, past behavior, and failure rates, would be risky to take on as clients. If left to their own devices, a salesperson would naturally not be very inclined to turn away a prospect. BI-informed rules can overrule our sales team if the prospect seems to have a high potential to fail based on past data.

This does not mean it is always a bad idea to take risks. Leaders should still rely upon their instinct if they strongly believe they are right. But comparing that gut-feeling with the data consensus is a good way to test the certainty of the decision.

Likewise, if a leader decides to go against the data, they must take ownership of that choice if things go badly, and bear responsibility for the outcome. Exceptions need to have accountability because, as the saying goes, “Success has many fathers, while failure is an orphan.”

Setting Rules and Policies

Of course, decision-making is executed at all levels of the organizational chart. While the executive team will handle decisions that make or break the business, successful companies ensure that employees are empowered to make decisions at every tier of the company. Where the data is overwhelming, leaders may choose to set guidelines based on evidence.

Another pertinent example at Acceleration Partners is our approach to counteroffers. In our experience, counteroffers have a poor short-term outcome because they adversely affect the relationship with the employee and only temporarily fix the underlying issues. For example, a study from Heidrick & Struggles found that 80% of senior executives think trust with an employee is diminished after the employee accepts a counteroffer.

Knowing this, we made a blanket policy for our talent team to not extend counteroffers. We think it’s a mistake to do something with such a high failure rate, and by setting a policy, we release less-experienced employees from making those hard choices without the benefit of the data or experience. We are playing the odds.

Educating employees on the historical odds of decisions prevents them from making unnecessarily risky decisions and gives leadership a chance to carefully consult the data and weigh the consequences and costs of failure.

Instinct still has a place in business, but it should not be the only driver of decision-making. By making data and BI a focal point of your team’s strategic thinking, and using it to craft smart organizational policies, leaders can safeguard their businesses against unnecessary failure, and ensure that the company makes more good decisions than bad.

Robert Glazer @ Harvard Business Review

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Planning projects accurately is notoriously difficult, whether they’re publicly or privately funded, or in domains like construction, technology, pharma, or infrastructure. According to the 2018 “Pulse of the Profession” study conducted by the Project Management Institute, between 2011 to 2018 only about 50% of projects where completed on time and approximately 55% were within budget. Even though firms have been investing in project management techniques since the 1970s, the accuracy of project plans has not improved much.

Inaccurate forecasts involving durations, costs, resources, and benefits are clearly major source of risk for leaders’ careers and organizations’ growth opportunities. For example, firms waste an average of $119 million for every $1 billion spent (11.9%) on projects due to poor project performance. Late or pricey projects can also affect the health of the economy at large. Gross domestic product (GDP) contributions from project-oriented industries are forecasted to reach $20.2 trillion by 2027; major missteps have the potential to chip away at this number.

Forty years ago, psychologist and Nobel prizewinner Daniel Kahneman, along with long-term collaborator Amos Tversky, noted that humans tend to suffer from a planning fallacy: they overpromise and underdeliver by offering unrealistic forecasts of projects’ objectives. Kahneman and Tversky suggested using an outside view to develop more realistic project plans. They proposed using a forecasting technique called reference class forecasting, by which projects’ durations or costs are predicted by comparing a project of interest to a set of past similar projects. Such as outside view is in contrast to the inside view that’s more often taken, where the project is planned with little regard to historical performance and its ability to meet set targets.

Today, changing attitudes toward data collection, data-driven prediction, and decision-making offers unprecedented opportunities in the field of project planning. With data, firms can now operationalize Kahneman and Tversky’s ideas, going beyond their original vision. Using historical data on projects’ initial forecasted completion dates and total costs, in addition to realized or actual expenditures and durations, accuracy estimates can be established. Such estimates can then be used when forecasting and setting new projects’ goals. Here are some examples of places where data-based prediction is occurring, as well as some publicly available resources you can use.

In the U.K., data on project performance has been collected for over a decade now. The HM Treasury’s Green Book provides guidance on how project proposals should be appraised before significant public funds are committed. The appraisal procedure includes an explicit adjustment to account for systematic optimism, sometimes referred to as “optimism bias,” which is the overstatement of benefits and the understatement of durations and costs.

These optimism adjustments use reference class forecasting and are constructed empirically based on historical data deemed relevant. In a study I conducted for the U.K.’s Department for Transport, along with researchers from University College London, Erasmus University Rotterdam, and Warwick Business School, we found that rail infrastructure projects require anywhere from a 64% optimism adjustment (for projects in early definition stages) to a 4% adjustment (for projects that have already completed detailed designs). These adjustments were determined by an analysis of data from thousands of historical projects.

Much of the Green Book’s guidance regarding optimism bias and required adjustments has been inspired by work done by Oxford professor Bent Flyvbjerg. Professor Flyvbjerg has collected data on hundreds of large-scale projects, mostly in the areas of infrastructure, construction, and information technology. Subsets of projects in this dataset serve as relevant reference points when appraising new initiatives.

In the U.S., the Program Management Improvement Accountability Act was signed into law on December 14, 2016. The Act, which aims to improve program and project management practices within the Federal Government, establishes initial guidance for coordinated and government-wide approaches to strengthen project management practices. Aimed at federal agencies, the goal is to improve government performance, including the “use of cost and schedule data to support decision-making.” In the future, this Act will hopefully serve as a significant catalyst to help establish resources for data collection. The fact that the government took this step could also help to establish new norms in project-related data collection.

In both of these examples, the set of projects that are considered in the reference class is identified by human judgement. What if artificial intelligence could help perform this role? Data availability and access to data-driven artificial intelligent algorithms can help take reference class forecasting to the next level, revolutionizing how projects are being planned and how targets are being set.

How? Using detailed project-plan-level data with information on individual tasks, deep learning and artificial intelligence can identify patterns of similarity among project tasks, hierarchies, and precedent relations. nPlan, a London-based startup, is doing this now. The company uses data from tens of thousands of construction projects involving millions of tasks combined with natural language processing techniques to predict project durations and delays. The combination of rich data and proprietary AI capabilities allows nPlan to generate highly accurate and useful forecasts for project completions dates, including information about the risks of delay. Here, AI algorithms learn which patterns are most useful for predicting delays, relaxing the need to declare a reference class upfront.

There have been those who suggest that the availability of data and AI technologies will introduce a “seismic shift” in project planning. Let’s hope that this shift will finally enable us to overcome the planning fallacy, too.

Yael Grushka-Cockayne @ Harvard Business Review

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The key to guiding your organization through this sort of digital transformation isn’t about finding the perfect technology solutions. It isn’t about one specific business intelligence (BI) tool or platform. And it isn’t about generating an all-encompassing dashboard or report. Sure, BI tools, dashboards and reports are all facets of digital transformation. But the real key is in guiding the cultural shift at your organization.

Step 1: Digital Transformation Starts With Culture

Spearheading the change in culture, at both the leadership level and across the broader organization, is your first priority. This should come before even selecting your technological approach to measurement. You want to ensure that leadership has the right mindset around how they’re making decisions—resources poured into measurement are worthless if leadership isn’t using data to drive business decisions. Moreover, it’s also important for your measurement/analytics team to have some knowledge of the decisions being driven by the data.

The takeaway: Before you begin thinking about specific technologies, make sure you’ve secured the proper buy-in across your organization and that your cultural is ready to handle an operational shift.

Step 2: Provide the Right Training

The next step is ensuring everyone has the proper training to understand how to implement analytics and/or garner insights from data.  This includes leadership and all employees who will view or use reports (for example, a Google Analytics report). Of course, you wouldn’t expect everyone to have the same level of expertise as your analytics team. However, everyone using a report should have at least a basic understanding of how that report was generated.

Here’s a simple, actionable way to get started: Ask everyone who will view reports to become Google Analytics certified. It only takes a few hours and will help deepen your team’s understanding of analytics in general and what’s possible to measure.

Step 3: Collectively Determine What’s Important to Measure

Before you can tackle the nuts and bolts of how you’re going to measure and collect data, you have to decide what data you’re going to collect and what metrics will be most valuable to your organization. This needs to be a collaborative decision between leadership and the analytics team. Leadership will ultimately be using the data to make business decisions. Meanwhile, the analytics team has in-depth knowledge of the full measurement process. From the start, leadership and analytics should be working collectively to determine what will be measured and what business decisions will be made based on those measurements.

Step 4: Make Sure Data Can Be Trusted

During a digital transformation, it’s possible to become overly focused on the powerful potential of analytics and miss sight of more fundamental aspects such as data validation. Be careful! It’s critical that by the time data is presented, it’s 100% trustworthy. Lack of trust in the accuracy of data is one of the top reasons why executives go back to making opinion-based decisions—they don’t believe they can trust the data. So before leaping ahead to something complex like, for instance, predictive analytics, make sure that you’ve laid the appropriate foundation and you trust the accuracy of the data you’re collecting. Focusing on Data Governance – knowing what data you’re collecting, how it’s labeled and where it’s stored, is  a great first step.

Step 5: Be Honest with Your Analytics

Finally, it’s important to use analytics honestly. Communicate data clearly both when your campaigns succeed and also when they fail. Measurement isn’t only about celebrating what’s working. It can be just as valuable to use insights to identify what’s not working and to improve the performance and return of your marketing strategy. Be honest and show what’s not working (and how you’re modifying your approach based on data). You will gain respect and trust by not only presenting the pretty angles.

Digital transformation isn’t a quick fix to a single problem. It’s an operational shift. It’s an iterative process and shouldn’t be rushed. But with the right approach, moving from opinion-based to data-driven decision making will ultimately lead to big wins where it matters — your organization’s bottom line.

Chiefmarketer

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Start with Value

We’re in an age where legacy-era ideas (and tech) co-exist with the multitude of new technologies and business models that digital transformation brings. As such, we can’t expect everyone on our board, or within our company, to understand tech jargon. What board members do understand: Business and Numbers. What your board needs to know about tech advancement starts with value. This is where analytics and other research can come into play. How much time will it save? How many processes can it improve? How much faster will you be able to hire a new recruit or close a business deal? None of these requires an explanation regarding how a certain algorithm works, or why you’re choosing the cloud or server technology that will work best for your company. What your board needs to know is simply what your company needs to operate successfully.

You’ve already sold employees and other key stakeholders on the value of tech, it shouldn’t be too difficult to tweak the narrative and sell it to the board too.

Keeping up with the Competition

The worst thing you can do in digital transformation is play ostrich—pretending you don’t see how rapidly your competitors and the greater industry are advancing 24/7. Just ask the taxi industry or movie rental business. The world of tech is working at a breakneck clip. If your company falls behind, the results will be exponentially terrible. That’s why what your board needs to know about tech includes what your competitors are already doing. Again, this shouldn’t be a list of the types of technology your competition is using. It should be about the ways your competitors are converting more leads, stealing your customers, reaching more people, personalizing marketing materials more effectively, and disrupting the industry. Yes, even if you’re afraid it will make you look bad, you need to be honest about where your company is falling short. That’s the only way to bring it up from behind.

Having an up to date competitor analysis is crucial. You should be prepared to explain what your competitors are doing at any time.

Brand Transparency is Good for Consumers and For You

I’ve said it before, but digital transformation is all about customer experience, and that experience translates into “brand on steroids” as customers share what they’ve experienced with your company on social media, Yelp, and by word of mouth. There is literally no stopping the truth about your brand from getting out in digital transformation. What your board needs to know about tech is that transparency is GOOD in digital transformation. It’s an opportunity for your brand to stand above the crowd by informing customers what kinds of data you’re tracking and offering them a chance to opt-out if they want to. It’s a chance to gain free advertising through happy customers sharing stories on social media. It’s a chance to create a brand that is easily identifiable—and to whom customers are insanely loyal. That’s what tech can do in digital transformation.

Especially in light of all of the data breaches in recent years and privacy issues that major companies have had, transparency has never been more important. Be more like Tylenol and less like Toyota. Be open and honest about the tech you use, the data you collect, and how you use it. Unless you have a proprietary secret like the recipe for Coca-Cola, I don’t see a lot of reason in hiding information from your consumers. While your board may be concerned about reputation, it’s critical to assert that not saying anything about your brand could be equally as harmful as saying too much.

Data-Driven Decisions

This should be a no-brainer for your board, but you may still experience resistance due to legacy-era (ego-driven) thinking. What your board needs to know about tech is that your company will be using it to make smarter, faster, more informed decisions. Data, technology, AI, machine learning, when used correctly, can help you decide where to build your next store and which product should be developed next, who you need to hire, if there’s a surplus in the supply chain, if employees are spending too much time on a project, if you’re spending too much on overtime, etc. There’s a multitude of information that you can glean from your technology that you use to advance your company. Your board needs to know that tech can also help scale your company. That Tech, when developed fully, can be trusted to grow your company in ways that human beings may never truly understand. That data is nothing without action. That your company will be using tech to make decisions because it must to stay competitive in today’s marketplace.

Pulling your board into the tech circle should enhance the momentum of your digital transformation, not detract from it. I’m not saying you need to add a bunch of tech execs to your board team, by any means. What I am saying is that there is a large chance your board will need some coaching so that they can support you and your company in the best way they possibly can. What your board needs to know about tech is at the very least, enough to prevent them from holding you back.

Forbes

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