Randy Bean’s book, “Fail Fast, Learn Faster” hits bookshelves this week. The book details the Big Data Revolution and the Chief Data Officers that emerged to manage the volume, variety, and velocity of data it unleashed.
Bean is clear that Big Data captured corporate imaginations unlike prior data movements, including ‘business intelligence.’
Being Data Driven Matters
Bean starts by asserting something that is becoming axiomatic: Data is in its ascendancy. And in this process, data has moved for the arcane to the mainstream.
Historically, Bean argues, data initiatives were primarily about market research, statistical analysis, and actuarial science (topics that were hard to talk about at cocktail parties.) During this time, specialists worked with small amounts of data to develop insights of limited business importance. In those days, data science was defined by statistics, probability, and operations research.
Today, the world races to become data-driven. However, being data-driven, as I’ve found in the #CIOChat, is about more than tooling. Success requires leadership and vision, not to mention a CEO that really groks data (as I’ve asserted in prior articles). Future firms with the ‘right to win’ will either be born with data in their DNA or realize their success depends on getting really good at data.
For large companies, this means capitalizing on breadth and depth of data. Jeanne Ross of MIT-CISR has suggested that the way legacy firms win against start-ups is by putting together their multi domains of data to solve bigger problems than startups can. For those that do this well, data delivers innovation and greater business agility. In this way, Bean argues, data acts like a market accelerant.
Big Data in the Trough of Disillusionment?
Bean claims Big Data became common around 2011. Bean echoes Judith Hurwitz (“Big Data for Dummies”) when he writes that Big Data refers to “extensive sources and repositories of data of many different forms and varieties.”
Such volume presents challenges. Bean quotes Michael Stonebraker, who said, “Big Data is less about managing the volume of data and much more about integrating the wide variety of data sources that are available—which can include data from legacy transactional systems, behavioral data sources, structured, and unstructured data and all sizes of data.”
Bean argues that Big Data captured the imagination of the C-Suites and boards unlike previous data initiatives. And while many organizations are clearly running into what analysts call the ‘trough of disillusionment’ (when a topic starts to receive less press and board attention), Bean argues that Big Data is still a big focus, as organizations are achieving Big Data benefits with no end in sight.
His book spotlights such organizations. Bean tells the stories of leading companies that have reported significant strides in achieving business outcomes, including American Family Insurance, Capital One, Charles Schwab, Cigna, Munich Re, Nationwide Insurance, and Travelers Insurance. For these organizations, Bean argues that Big Data has empowered their data users to experiment, fail and learn quickly from their mistakes so that they may move forward with speed, agility, and confidence.
Sabotaging the Success of Big Data
Recently, talk of data pipelines and supermarkets has grown popular. Many stress the importance of a ‘data pipeline’, which “gathers, inputs, cleans, integrates, processes, and safeguards data in a systematic, sustainable, and scalable way” (Iansanti, M. and Lakhani, K. Competing in AI). The goal is to create a data supermarket. Yet pipelines and supermarkets overlook the reality for most businesses. And while Bean acknowledges the importance of making data accessible and cataloged, he suggests that most companies are still striving to leverage data as a business asset and forge data culture.
This takes time. A big problem in terms of treating data as a strategic asset is that legacy firms have difficulty justifying initiatives that are long-term and strategic. For this reason, Bean suggests data initiatives often become siloed, or overly compliance- or cost-focused. This of courses makes data and analytics more about the back office than the front office.
Unfortunately, a short-term view undercuts a business’s long-term interests. Bean stresses that acceptance of change starts with people. We have talked regularly in the #CIOChat that, when it comes to catalyzing change, it is always people, process, and then technology — regardless of how big the technology issues are. Clearly, Drucker and Bean are right when they stress that “culture eats strategy for breakfast.”
Not surprisingly, the “Big Data and AI Executive Survey for 2021,” run by Bean’s firm, found that “cultural factors were cited by 92% as the principal issue that organizations face in becoming data centric.” Other issues include forest-versus-trees thinking or being stuck in the weeds. Clearly, we need more data-driven executives like Target’s Brian Cornell. Data-driven CEOs, CDOs and CIOs can secure the executive commitments needed. With these in hand, it is critical that data leaders establish realistic commitment from a supportive executive team.
For the above reasons, it came as no surprise that only 39.3% are managing data as an asset according to this survey. The big-picture problem, Bean argues, is that a legacy data environment, business processes, skill sets, and traditional cultures are reluctant to change, and they get in the way.
However, Bean stresses, “it is not enough for companies to embrace modern digital architectures, agile methodologies, and integrated data initiatives… when less than a quarter of executives report their organizations have successfully forged a data culture.”
Practices for Companies Winning at Data
Once organizations solve cultural issues among people, Bean says that companies must alter or update their data practices, which are often steeped in outdated business approaches. Updated thinking at minimum should include the following:
- Data is a shared asset
- Data must be governed
- Data is consumed by many
- Data ownership is a shared responsibility
- Data success requires partnership
- Data forces us to think differently
Doing this right requires a data strategy, data governance, data management, change management, and business adoption.
Chief Data Officers
As Big Data became mainstream, Bean argues, many organizations concluded they needed a a data czar. And this always turned out to be the company’s CIO. Early CDOs focused upon data quality, data accuracy, transparency, privacy, and reporting. They were, to use Tom Davenport’s nomenclature, focused upon data defense.
The CDO role continues to evolve. Bean claims today CDOs are established — but not yet on firm ground. Part of the reason for this is there isn’t yet a uniform definition for what a CDO does. With that said, the good news is that CDOs are increasingly moving onto data offense. Whereas defense is often focused on compliance, offense focuses on business impact and data monetization. Indeed, 70% of leaders in 2021 are focused on revenue generating activities, according to the Executive survey.
This is good news, yet in truth, data management still remains nascent at most firms. An inability to forge a data-driven culture looms as a large impediment to success. Research from MIT-CISR finds that 51% of legacy organizations are still in silos and 21% in duct tape. For these organizations, Bean says data challenges will only grow as their volume and variety of data increases.
Successful CDOs and CIOs need not only speak about data and analytics in the language of business but relentlessly communicate on the value of data projects delivered. Mastery of outreach and communication is essential, regardless of who is the data leader.
Connection from Data to Digital Transformation
A majority of business transformations start with data, Bean asserts. He quotes Stonebreaker, who says “The future on data management lies in data curation which is being aimed at the hundreds or thousands of data silos not captured within the traditional data warehouse, and which can only be captured and integrated at scale by applying automation and machine learning based on statistical patterns. Data curation relies upon machine learning systems that use statistical techniques to learn and build knowledge over time.”
For Bean, the opportunity of transformation is largely about optimizing operational processes. Iansanti and Lakhani, similarly say “AI is becoming a universal engine of execution.” But they go further, saying digital transformation “transforms the very nature of companies and how they compete.” For Iansanti and Lakhani, data and AI enable what they call digital scale, digital scope, and digital learning. This means rethinking, not just a firm’s operating model, but also its business model.
According to Jeanne Ross, Cynthia Beath, and Martin Mocker in “Designed for Digital”, there are 5 digital technologies relevant to digital transformation: social, mobile, analytics, cloud, and the Internet of Things.
In the digital world, you have ubiquitous data, unlimited connectivity, and massive processing power, Ross and company point out. “In this world, ubiquitous data means you don’t guess what customers want, or who they are, or whether they are loyal.” They go on to suggest that a digital value proposition is a ‘digital offering’. Digital offerings allow for maximum experimentation by combining data, business logic, and infrastructure to solve specific customer problems.
Bean’s book is worth the read for anyone concerned about data or digital transformation. Some of the case studies are great fodder for organizations started or embarked on their data journeys.
Clearly, people and culture really matter. And Bean’s suggestions on what things to tackle when offer a roadmap for leaders focused on achieving a data culture alongside their digital transformation.