Organizations can gain more business value from advanced analytics by recognizing and overcoming five common obstacles.

More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning.1 Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so.2

To better understand the mistakes that companies make when implementing profitable data science projects, and discover how to avoid them, we conducted in-depth studies of the data science activities in three of India’s top 10 private-sector banks with well-established analytics departments. We identified five common mistakes, as exemplified by the following cases we encountered, and below we suggest corresponding solutions to address them.

Mistake 1: The Hammer in Search of a Nail

Hiren, a recently hired data scientist in one of the banks we studied, is the kind of analytics wizard that organizations covet.3 He is especially taken with the k-nearest neighbors algorithm, which is useful for identifying and classifying clusters of data. “I have applied k-nearest neighbors to several simulated data sets during my studies,” he told us, “and I can’t wait to apply it to the real data soon.”

Hiren did exactly that a few months later, when he used the k-nearest neighbors algorithm to identify especially profitable industry segments within the bank’s portfolio of business checking accounts. His recommendation to the business checking accounts team: Target two of the portfolio’s 33 industry segments.

This conclusion underwhelmed the business team members. They already knew about these segments and were able to ascertain segment profitability with simple back-of-the-envelope calculations. Using the k-nearest neighbors algorithm for this task was like using a guided missile when a pellet gun would have sufficed.

In this case and some others we examined in all three banks, the failure to achieve business value resulted from an infatuation with data science solutions. This failure can play out in several ways. In Hiren’s case, the problem did not require such an elaborate solution. In other situations, we saw the successful use of a data science solution in one arena become the justification for its use in another arena in which it wasn’t as appropriate or effective. In short, this mistake does not arise from the technical execution of the analytical technique; it arises from its misapplication.

After Hiren developed a deeper understanding of the business, he returned to the team with a new recommendation: Again, he proposed using the k-nearest neighbors algorithm, but this time at the customer level instead of the industry level. This proved to be a much better fit, and it resulted in new insights that allowed the team to target as-yet untapped customer segments. The same algorithm in a more appropriate context offered a much greater potential for realizing business value.

It’s not exactly rocket science to observe that analytical solutions are likely to work best when they are developed and applied in a way that is sensitive to the business context. But we found that data science does seem like rocket science to many managers. Dazzled by the high-tech aura of analytics, they can lose sight of context. This was more likely, we discovered, when managers saw a solution work well elsewhere, or when the solution was accompanied by an intriguing label, such as “AI” or “machine learning.” Data scientists, who were typically focused on the analytical methods, often could not or, at any rate, did not provide a more holistic perspective.

To combat this problem, senior managers at the banks in our study often turned to training. At one bank, data science recruits were required to take product training courses taught by domain experts alongside product relationship manager trainees. This bank also offered data science training tailored for business managers at all levels and taught by the head of the data science unit. The curriculum included basic analytics concepts, with an emphasis on questions to ask about specific solution techniques and where the techniques should or should not be used. In general, the training interventions designed to address this problem aimed to facilitate the cross-fertilization of knowledge among data scientists, business managers, and domain experts and help them develop a better understanding of one another’s jobs.

In related fieldwork, we have also seen process-based fixes for avoiding the mistake of jumping too quickly to a favored solution. One large U.S.-based aerospace company uses an approach it calls the Seven Ways, which requires that teams identify and compare at least seven possible solution approaches and then explicitly justify their final selection.

Mistake 2: Unrecognized Sources of Bias

Pranav, a data scientist with expertise in statistical modeling, was developing an algorithm aimed at producing a recommendation for the underwriters responsible for approving secured loans to small and medium-sized enterprises. Using the credit approval memos (CAMs) for all loan applications processed over the previous 10 years, he compared the borrowers’ financial health at the time of their application with their current financial status. Within a couple of months, Pranav had a software tool built around a highly accurate model, which the underwriting team implemented.

Unfortunately, after six months, it became clear that the delinquency rates on the loans were higher after the tool was implemented than before. Perplexed, senior managers assigned an experienced underwriter to work with Pranav to figure out what had gone wrong.

The epiphany came when the underwriter discovered that the input data came from CAMs. What the underwriter knew, but Pranav hadn’t, was that CAMs were prepared only for loans that had already been prescreened by experienced relationship managers and were very likely to be approved. Data from loan applications rejected at the prescreening stage was not used in the development of the model, which produced a huge selection bias. This bias led Pranav to miss the import of a critical decision parameter: bounced checks. Unsurprisingly, there were very few instances of bounced checks among the borrowers whom relationship managers had prescreened.

The technical fix in this case was easy: Pranav added data on loan applications rejected in prescreening, and the “bounced checks” parameter became an important element in his model. The tool began to work as intended.

The bigger problem for companies seeking to achieve business value from data science is how to discern such sources of bias upfront and ensure that they do not creep into models in the first place. This is challenging because laypeople — and sometimes analytics experts themselves — can’t easily tell how the “black box” of analytics generates output. And analytics experts who do understand the black box often do not recognize the biases embedded in the raw data they use.

The banks in our study avoid unrecognized bias by requiring that data scientists become more familiar with the sources of the data they use in their models. For instance, we saw one data scientist spend a month in a branch shadowing a relationship manager to identify the data needed to ensure that a model produced accurate results.

We also saw a project team composed of data scientists and business professionals use a formal bias-avoidance process, in which they identified potential predictor variables and their data sources and then scrutinized each for potential biases. The objective of this process was to question assumptions and otherwise “deodorize” the data — thus avoiding problems that can arise from the circumstances in which the data was created or gathered.4

Mistake 3: Right Solution, Wrong Time

Kartik, a data scientist with expertise in machine learning, spent a month developing a sophisticated model for analyzing savings account attrition, and he then spent three more months fine-tuning it to improve its accuracy. When he shared the final product with the savings account product team, they were impressed, but they could not sponsor its implementation because their annual budget had already been expended.

Eager to avoid the same result the following year, Kartik presented his model to the product team before the budgeting cycle began. But now the team’s mandate from senior management had shifted from account retention to account acquisition. Again, the team was unable to sponsor a project based on Kartik’s model.

In his third year of trying, Kartik finally got approval for the project, but he had little to celebrate. “Now they want to implement it,” he told us, with evident dismay, “but the model has decayed and I will need to build it again!”

The mistake that blocks banks from achieving value in cases like this is a lack of synchronization between data science and the priorities and processes of the business. To avoid it, better links between data science and the strategies and systems of the business are needed.

Senior executives can ensure the alignment of data science activities with organizational strategies and systems by more tightly integrating data science practices and data scientists with the business in physical, structural, and process terms. For example, one bank embedded data scientists in business teams on a project basis. In this way, the data scientists rubbed elbows with the business team day to day, becoming more aware of its priorities and deadlines — and in some cases actually anticipating unarticulated business needs. We have also seen data science teams colocated with business teams, as well as the use of process mandates, such as requiring that project activities be conducted at the business team’s location or that data scientists be included in business team meetings and activities.

Generally speaking, data scientists ought to be concentrating their efforts on the problems deemed most important by business leaders.5 But there is a caveat: Sometimes data science produces unexpected insights that should be brought to the attention of senior leaders, regardless of whether they align with current priorities.6 So, there is a line to be walked here. If an insight arises that does not fit current priorities and systems but nonetheless could deliver significant value to the company, it is incumbent upon data scientists to communicate this to management.

We found that to facilitate exploratory work, bank executives sometimes assigned additional data scientists to project teams. These data scientists did not colocate and were instructed not to concern themselves with team priorities. On the contrary, they were tasked with building alternative solutions related to the project. If these solutions turned out to be viable, the head of the data science unit pitched them to senior management. This dual approach recognizes the epistemic interdependence between the data science and business professionals — a scenario in which data science seeks to address today’s business needs as well as detect opportunities to innovate and transform current business practices.7 Both roles are important, if data science is to realize as much business value as possible.

Mistake 4: Right Tool, Wrong User

Sophia, a business analyst, worked with her team to develop a recommendation engine capable of offering accurately targeted new products and services to the bank’s customers. With assistance from the marketing team, the recommender was added to the bank’s mobile wallet app, internet banking site, and emails. But the anticipated new business never materialized: Customer uptake of the product suggestions was much lower than anticipated.

To discover why, the bank’s telemarketers surveyed a sample of customers who did not purchase the new products. The mystery was quickly solved: Many customers doubted the credibility of recommendations delivered through apps, websites, and emails.

Still looking for answers, Sophia visited several of the bank’s branches, where she was surprised to discover the high degree of trust customers appeared to place in the advice of relationship managers (RMs). A few informal experiments convinced her that customers would be much more likely to accept the recommendation engine’s suggestions when presented in the branch by an RM. Realizing that the problem wasn’t the recommender’s model but the delivery mode of the recommendations, Sophia met with the senior leaders in branch banking and proposed relaunching the recommendation engine as a tool to support product sales through the RMs. The redesigned initiative was a huge success.

The difficulties Sophia encountered highlight the need to pay attention to how the outputs of analytical tools are communicated and used. To generate full value for customers and the business, user experience analysis should be included in the data science design process. At the very least, user testing should be an explicit part of the data science project life cycle. Better yet, a data science practice could be positioned within a human-centered design frame. In addition to user testing, such a frame could mandate user research on the front end of the data science process.

While we did not see instances of data science embedded within design thinking or other human-centered design practices in this study, we did find that the shadowing procedures described above sometimes operated as a kind of user experience analysis. As data scientists shadowed other employees to understand the sources of data, they also gained an understanding of users and channels through which solutions could be delivered. In short, the use of shadowing in data science projects contributes to a better understanding of the processes that generate data, and of solution users and delivery channels.

Mistake 5: The Rocky Last Mile

The bank’s “win-back” initiative, which was aimed at recovering lost customers, had made no progress for months. And that day’s meeting between the data scientists and the product managers, which was supposed to get the initiative back on track, was not going well either.

Data scientists Dhara and Viral were focused on how to identify which lost customers were most likely to return to the bank, but product managers Anish and Jalpa wanted to discuss the details of the campaign to come and were pushing the data scientists to take responsibility for its implementation immediately. After the meeting adjourned without a breakthrough, Viral vented his frustration to Dhara: “If data scientists and analysts do everything, why does the bank need product managers? Our job is to develop an analytical solution; it’s their job to execute.”

By the next meeting, though, Viral seemed to have changed his mind. He made a determined effort to understand why the product managers kept insisting that the data scientists take responsibility for implementation. He discovered that on multiple occasions in the past, the information systems department had given the bank’s product managers lists of customers to target for win-back that had not resulted in a successful campaign. It turned out that using the lists had been extremely challenging, partly due to an inability to track customer contacts — so the product managers felt that being given another list of target customers was simply setting them up for another failure.

With this newfound understanding of the problem from the point of view of the product managers, Viral and Dhara added to their project plan the development of a front-end software application for the bank’s telemarketers, email management teams, branch banking staff, and assets teams. This provided them with a tool where they could feed information from their interactions with customers and make better use of the lists provided by the data science team. Finally, the project moved ahead.

Viral and Dhara’s actions required an unusual degree of empathy and initiative. They stepped out of their roles as data scientists and acted more like project leaders. But companies probably should not depend on data scientists in this way, and they may not want to — after all, the technical expertise of data scientists is a scarce and expensive resource.

Instead, companies can involve data scientists in the implementation of solutions. One bank in our study achieved this by adding estimates of the business value delivered by data scientists’ solutions to their performance evaluations. This motivated data scientists to ensure the successful implementation of their solutions. The bank’s executives acknowledged that this sometimes caused data scientists to operate too far outside their assigned responsibilities. However, they believed that ensuring value delivery justified the diversion of data science resources, and that it could be corrected on a case-by-case basis, if the negative impact on the core responsibilities of data scientists became excessive.

The mistakes we identified invariably occurred at the interfaces between the data science function and the business at large. This suggests that leaders should be adopting and promoting a broader conception of the role of data science within their companies — one that includes a higher degree of coordination between data scientists and employees responsible for problem diagnostics, process administration, and solution implementation. This tighter linkage can be achieved through a variety of means, including training, shadowing, colocating, and offering formal incentives. Its payoff will be fewer solution failures, shorter project cycle times, and, ultimately, the attainment of greater business value.

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Leading Mobile Cloud-based Business Operating system provider, Domo, is taking the Connected Devices route to bridge Sales and Marketing processes. In an exciting development for Marketing and Sales Technology aficionados, Domo has announced a strategic partnership with MarTech giant, Zendesk. This partnership with Zendesk will help Domo customers to better manage their IoT solutions and offer a better, proactive service experience to their end users.

How IoT App is a Value-Add to Domo Customers?

This new IoT app extends the Domo IoT Cloud provisions, enabling a better, proactive customer service experience with Zendesk. This is particularly focussed at creating a self-service autonomous workflow from device data to issue resolution.

Domo is already recognized as a leader in Self-Service BI Market by independent market research firms and analysts. This new IoT App further strengthens the Company’s BI product roadmap with Zendesk as a key partner.

Why you’ll love your Zendesk data in Domo

Modern customers expect an unprecedented level of experience delivered on every device they access. It’s easy to promise on Customer Experience dreams than to truly deliver on these. This Domo IoT App allows customers to move out of the vacuum created due to Marketing, Sales and Customer Service silos.

Intricately tying into every business operation, Zendesk’s Domo integration allow Domo customers to connect and manage all your other sources of customer data within Domo. This means you can get a holistic view of your customers’ needs and habits. With that kind of information, you won’t merely respond to helpdesk requests.

Role of Zendesk in Domo IoT App

With this new app, companies can merge their Zendesk data with data from other sources, all in the Domo platform. Once this data is in Domo, companies can get a holistic view of its customers’ needs and habits, and see how business decisions are affecting customers in real-time.

Benefits of Working with Zendesk within Domo

With the new IoT app, you can merge Zendesk data with data from any other source, all in one platform.

Additionally, you can create real-time alerts and receive instant notifications when Zendesk metrics hit thresholds that you determine.

The most satisfying benefit of having Zendesk work within Domo is the availability of Instant insights. Domo customers can now bring Zendesk and other data sources together to see how business decisions affect customers in real-time.

Marketing and Sales will Relish this Fluent Combination of CRM Data with IoT Functionality

Companies get real-time alerts based upon changes in the data coming from a device, and Domo pushes that data back to Zendesk Sunshine, the company’s CRM platform built on Amazon Web Services (AWS), to automatically initiate a ticket for Zendesk support. This app offers an end-to-end solution for companies to take action based upon the data from their IoT machines.

At the time of this announcement, Jay Heglar, Chief Strategy Officer at Domo., said, “Domo is helping customers evolve their IoT data from an operational tool for the shop floor to a valuable asset for the entire company.”

Jay added, “With this new App, we are providing our mutual customers with a seamless experience for taking data from machines and initiating (a) workflow to solve problems. Zendesk Sunshine makes it easy to deliver this integrated experience, which we believe is the future of IoT.”

Domo Customers Enjoy An Upper-Hand in Dealing with Heightened Customer Expectations

Tech does all the talking in this Zendesk+Domo IoT app function. Customers can leverage Domo’s new IoT App to merge an organization’s most relevant Zendesk data points and turns them into dynamic visualizations. These include:

Meet Customer Satisfaction Objectives

This feature allows users to easily analyze customer survey responses and breakdown the data by marketing channel, manager, location, teams, and individual agents, to identify trends and improve customer satisfaction scores.

Support Ticket Activity

This feature allows customers to view all of their support ticket data in one digestible dashboard, seeing opened and closed tickets, the number of escalations, and support team efficiency with a variety of filters and segments.

Autonomous Zendesk QuickStart

The Zendesk QuickStart provides a comprehensive summary of team performance, including backlog management, ticket handling, agent leaderboards, and much more. This feature allows customers to optimize their customer support and streamline their organization’s operations.

Norm Gennaro, SVP of worldwide sales at Zendesk, said, “The best customer experiences are built on Zendesk. Our customers are looking to drive excellent customer experiences and increased business value through IoT data, our new partnership with Domo helps us deliver on that opportunity.”

Norm added, “With the new Domo and Zendesk app, organizations can gain a holistic view of their customers’ needs and habits, and anticipate helpdesk requests that are affecting their customers in real-time.”

What Domo’s Loyal Customers Have to Say about  the new IoT Applications for Their Marketing, Sales, and Service

Companies like SharkNinja will be able to use this new solution to provide a new level of customer service to its install base.

“When SharkNinja launched the new Ion robot, they had hundreds of thousands of robots in consumers’ homes. And it would take them a whole week to analyze data from just 20 robots to learn things like ‘is the battery running low,’ or ‘is the robot completing its mission.’

Domo already offers its unique Data Science suite to customers. Adding its IoT App to the marketplace will heighten its sellability quotient, especially with Zendesk as its tech partner in the ecosystem.

Domo currently has over 30 IoT Integration Apps including AWS IoT Core, Azure IoT Hub, Raspberry Pi, and Soracom.

Besides the Zendesk integration, the newest in the fleet of IoT Integration Apps is the MQTT data and connectivity App. MQTT is a machine-to-machine, Internet of Things connectivity protocol. This protocol is lightweight and is suitable for constrained environments. This protocol is deployed in many IoT devices globally. MQTT is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium.

For example, MQTT has been used in sensors communicating to a broker via satellite link, over occasional dial-up connections with healthcare providers, and in a range of home automation and small device scenarios. It is also ideal for mobile applications because of its small size, low power usage, minimized data packets, and efficient distribution of information to one or many receivers.

Currently, Domo’s mission is to be the operating system for business, digitally connecting all your people, your data and your systems, empowering them to collaborate better, make better decisions and be more efficient, right from their phones. Domo works with many of the world’s leading and most progressive brands across multiple industries including retail, media and entertainment, manufacturing, finance and more.

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Independent spend management company JAGGAER has announced a partnership with EdgeVerve Systems, a subsidiary of Infosys, to develop software products for customers in multiple industries including manufacturing, retail, logistics and higher education. EdgeVerve offers AI capabilities with Infosys Nia, and RPA capabilities with AssistEdge. The solutions will be delivered to customers through the JAGGAER ONE spend management solution suite.

EdgeVerve’s Nia and AssistEdge are enhancements to the JAGGAER ONE SaaS cloud-based platform, delivering technology to automate repetitive and rule-based human processes. This includes the automation of “high touch” processes through modelling rules into software robots that run non-intrusively on JAGGAER ONE. Utilising the same validation, security and data protocols ensure that ‘change management’ within organizations is minimal. Additionally, EdgeVerve RPA integrates with third party services to pass data between systems when no traditional application interface exists.

“EdgeVerve brings powerful processing to our platform with a solution that is already a leading product in the global RPA market. Where most providers utilize RPA in place of API to patch a broken and inefficient process, EdgeVerve delivers critical business functions such as massive document validation, which automates supplier management, and accelerates manual processes through digital transformation. The Nia AI platform collaboration with the JAGGAER ONE platform delivers sophisticated and industry leading automation to resource intensive functions,” said Zia Zahiri, CTO of JAGGAER.

“Procurement organizations are in the midst of a digital revolution to boost efficiencies and drive down costs. This partnership will be transformative in nature with two industry leaders, JAGGAER and EdgeVerve, coming together to collaborate and maximize business value for customers in the procurement space by leveraging Intelligent Automation and AI. Our partner program, “Synergy”, has been built on the foundation of collaboration to create mutual and customer value, and the partnership with JAGGAER is a testament to that philosophy,” said Atul Soneja, SVP & Global Head – Edge Products and Infosys Nia.

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