Data, analytics, and digital transformation
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Source: CogWorld Think Tank on VentureBeat
Accurate, complete, and timely data has always been required for success with digital programs. This is even more the case when it comes to large, enterprise-wide digital transformations. Yet, a recent New Vantage survey reported that just 24% of respondents they thought their organization was data-driven, a decline from 37.8% the prior year. Just as analytical tools are becoming in widespread use, requiring even more reliable data, it’s becoming increasingly difficult to be a data-driven company. Puzzling, isn’t it?
What is the reason for this plunge in becoming data driven? The same New Vantage survey reported that cultural challenges — not technological ones — represented the largest impediment and as many as 92.2% of mainstream companies reported that they have struggled with issues such as organizational alignment, business processes, change management, communication, skill sets, and resistance to change.
There is no shortage of advice on how to become more data driven. For example, SAS and TDWI suggest that better collaboration, improved data quality, and a greater focus on governance are part of the answer. Thomas H. Davenport and Nitin Mittal recommended in Harvard Business Review last year that the initiative be driven top down and that organizations pay attention to the use of cross-functional teams, along with other factors such as leading by example, providing specialized training and using analytics to help employees.
Why is it so hard?
Most executives acknowledge the importance of data in digital transformation, but when it comes to their own decision making, they are more likely to make intuition and gut feel driven decisions. After all, it’s their many years of experience that has landed them in their position of authority — isn’t it? Also, gathering high quality data can be problematic as department heads have hoarded data for decades in hard to access excel spreadsheets and the IT applications which have often been developed to meet specific departmental needs don’t communicate well with one another. Moreover, bridging data silos is difficult as such initiatives tend to rely on the IT department, which often has other more pressing priorities. Also, doing the analysis takes time — and it’s quite complicated. The amount of patience needed to overcome the challenges of data transparency and the patience needed in waiting for the time it takes to carry out analytics are not commonly observed traits of typical executive behavior. While there is no one universal recipe, paying attention to organizational alignment, cross functional business processes, and executive education is likely to improve the odds of success.
Improving alignment
Most executives today would agree that organizational alignment is important. In theory, strategies, organizational capabilities, resources, and management systems should all be arranged to support the enterprise’s purpose. In practice, when it comes to digital transformation — let’s just say — it’s complicated. When individual departments place greater emphasis on their own strategy than that of the organization — then alignment suffers. When there is a greater focus on variance to budget performance by department as opposed to customer value creation – then alignment weakens. This is particularly pertinent to digital transformation, as strategy — not technology — drives digital transformations. Only the CEO can provide the needed momentum to improve organizational alignment by instructing department heads to work together in crafting a company wide strategy and acting in unison on gathering the right data as well as measuring what matters.
Addressing process issues
If an organization focuses solely on workflow and processes inside of departmental boundaries — then fragmentation drives data transparency issues, and data driven decisions suffer. An enterprise wide, high level process context is needed to overcome such fragmentation. According to one recent survey 26% of survey respondents said they have any data strategy at all, and 70% don’t have what they consider to be a mature data strategy. A back-to-basics approach is useful in creating a high-level process context with a focus on the core activities of getting products/services developed, made, sold and delivered. This approach would highlight the 12 to 16 end-to-end processes that typically determine organizational capability for most firms. A linear depiction of these processes is not enough. An effective framework must also draw attention to the activities, the cross functional roles and the applications and data needed for exceptional performance.
Most organizations will find that paying attention to key cross functional processes such as “order to delivery”, “request to resolution” and “idea to launch” can pay huge dividends in terms of identifying what data is needed for digital success and at the same time improving customer experience. Similarly, focusing on the key internal business processes that have a major impact on employee experience, such as “requisition to onboard” and “requirements to implementation” can create the right context and the needed focus to drive a data driven approach. The right foundation is created by getting people from the various departments involved in such cross functional business processes to work together in data driven environment to solve problems that are known to matter. For example, in the “order to delivery” process, collaboration is typically needed between sales, operations and customer service.
So, it’s not just about forming cross-functional teams that combine people with different backgrounds such as data analytics, business, and technology — although that’s important too. It’s also about the right context that creates focus, drives cross functional collaboration and management attention on highly visible business issues that is even more valuable. This approach is far superior to viewing data requirements one department at a time.
Providing executive training
There’s no shortage of courses on data and analytics. Wharton, the University of Toronto, and MIT are just a few of the prestigious universities with solid offerings. There’s just one problem — data and analytics can be boring in the abstract. That’s why it’s important to apply analytics to real, pressing problems in the context of end-to-end processes. However, so doing takes both a systemic and systematic approach to big data and analytics in a big picture context of digital transformation. That is sometimes challenging as both CEOs and IT departments are often busy putting out fires — but it can be done with discipline. To improve the odds of success, SAS recommends paying attention to factors such as a balanced focus on developing business skills as well as technical skills, discipline in performance measurement, and an accelerated approach to change management.
How are you doing?
Instead of just thinking about deploying a given individual technology tool for the benefit of an individual department, leaders need to shift attention to deploying multiple tools with reliable, accessible data in an integrated, agile manner for the benefit of customers and the business.
Focusing on customer experience and a set of highly visible business problems or opportunities in a process context form the foundation for data driven digital transformation. That’s quite different than a traditional, siloed, departmental approach and involves an outside-in view to drive cross functional collaboration.
How are you doing? Consider answering the following questions.
Do individual departments place greater emphasis on their own strategy than that of the organization?
Is process modeling primarily focused on small processes inside of departmental boundaries?
Do process improvement projects tend to have small, incremental improvement goals?
Do key performance indicators (KPI’s) have a visible bias towards volume and cost?
Are your executives more concerned about their department than on creating value for customers?
Is organization wide restructuring carried out frequently?
Do department heads view one another as competitors for the top job as opposed to collaborators?
Are IT projects often launched and executed in response to individual departmental needs?
If you answered “YES” to four or more of the above questions, then your company may find it particularly challenging to apply data-based decision making in your digital programs.
You are probably not alone. Tom Davenport and Randy Bean have been reporting on data driven transformations for over 8 years and found that companies continue to struggle despite substantial investments in technology and applications. Paying attention to organizational alignment, cross functional business processes, and executive education can change the odds of success.
Andrew Spanyi is President of Spanyi International. He is a member of the Board of Advisors at the Association of Business Process Professionals and has been an instructor at the BPM Institute. He is also a member of the Cognitive World Think Tank on enterprise AI.