Data Synergy For Business and IT
This is a four-part series.
Part one: Deriving actual value from data
Part two: Being “data-driven” (but not in a good way)
Part three: Aligning people, process and data
Part four: Data synergy for Business and IT
“An investment in knowledge pays the best interest.”
Is anyone interested in arguing that point? (not us.)
Do you still want to be data-driven? (we thought not.)
In the chase for leadership positions – regardless of industries - companies make enormous investments in technology to collect and “use” data to gain an upper-hand in their respective markets. However, based on the research we have discussed in earlier writings, we do not believe Ben would have approved of those investments based on the fuzzy outcomes or vague value delivered.
Cases in point:
A large US Healthcare Insurance company’s IT department struggled with ever-increasing performance demands from the business users for data. After assessing the cost to meet the growing demand, the IT organization realized they could not afford to upgrade and scale their existing “big 3” merchant data warehouse, wasting the past four years of development. Instead, they opted to move the warehouse to a cloud-based Hadoop solution. After all, Hadoop scales easily, and the cloud offered “limitless” processing power.
The IT group spent another two years moving off the merchant platform and into Hadoop in the cloud. At this point, the business users revolted because they had no time or inclination to use limiting tools and processes in the Hadoop world to access their data.
The IT group then began to look at MongoDB and Redshift as alternative solutions for the end-users. 8+ years and counting for the business to effectively access and use data.
Would a data strategy have helped both IT and the business operate more effectively?
We would certainly say so. And one more thing – the business users' number one priority in the example above has been to create a “Customer 360” view of their constituents for years – made even more critical during a pandemic. How close are they? -- 8+ years and counting.
Our second example comes from a business group at a large US Fortune 500 company. One of the senior executives felt that including graphics along with the transactions would optimize some manual tasks the IT group currently supported across applications. The general consensus was that IT lagged in supporting the business users’ analytic needs and this integration might alleviate some of the backlogged work and antiquated processes.
In an admirable but misguided effort, the business and IT organization jointly approached the problem and began building the company’s first “modern” data warehouse. In assessing potential platforms to support a “mixed-data mode” storage environment, they opted for a NoSQL solution from MarkLogic. Several years and millions of dollars invested in the project resulted in a successful deployment of the MarkLogic platform. Unfortunately, they quickly realized the solution would not support the broader corporate needs for standardized reporting. While the platform did integrate video, graphics, and text into one platform nicely, it did not effectively support the business's actual needs at an analytic reporting level. At an added cost to the project’s price tag, they purchased a solution from Qlik to enable those broader reporting needs.
The result: millions spent, standard reports recreated/redelivered (after three years), and no real added value realized since IT now had to support extending data out into Qlik. Would a data strategy have helped? Most certainly. As an added frustration, the company's long-term goal is to have the business operate in a self-service model and use limited data copies. How close are they to that goal? Three-plus years and counting, as the added Qlik environment only worsened the problem by creating yet another single-use stove-piped data environment.
There are, of course, hundreds of other examples of deployments gone astray. Admittedly, a good data strategy might not prevent all shortcomings/failures, but the deployment of a broader strategy before engaging on an individual “project” approach would have had positive impacts on the result. Important to note, neither the business nor IT bears complete blame for data environments that go astray. For decades, IT has gone above and beyond to make data available to end-users by often stretching existing technology to its limits and beyond. But as we noted in earlier discussion, IT is driven by cost-control metrics, and not value drivers. In today’s world, taking a more thoughtful, pragmatic approach is paramount to success since the technology options available to IT are more disperse and powerful than ever before. The onus to be more proactive and involved in aiding with the data deployment strategy falls squarely on the business user’s shoulders - to be more educated and concise in their data needs, goals, and objectives.
“Ray Kinsella: “…I have done everything that I’ve been asked to do! I didn’t understand it, but I’ve done it; and I haven’t once asked ‘what’s in it for me?’”
Shoeless Joe Jackson: “What are you saying, Ray?”
Ray Kinsella: “I’m saying, what’s in it for me?””
We have hammed quite a bit on the problems and potential solutions a sound data strategy offers a company or organization. But what most folks want answered by this point of the discussion is the “What’s in for me” question. The answer lies in the simple process graphic below:
While the items listed may seem high-level or even intuitively obvious, we continue to be amazed that companies cannot point to many, if any, and say they operate within those constructs. They may claim them to be fundamental in their deployment strategies, but we find they often fall between the cracks when reviewing the environment holistically.
The data strategy is the missing element that amalgamates Business and IT.
Without it, companies will continue to be data-driven and not knowledge-driven in their pursuit of leadership positions in their industry segments.
““Would you tell me, please, which way I ought to go from here?” asked Alice.
”That depends a good deal on where you want to get to,” said the Cheshire Cat.”
In wrapping up this blog series, we at Marcavel believe that the Cheshire Cat asks one of the most fundamental and straightforward questions linked directly to success (and one we echo frequently to our customers): “Where DO you want to get to?”.
Interestingly, we find the answer is often radically different when posed to business and IT. And that organizational schism is where things begin to fall apart.
Some summarize Carroll’s exchange between Alice and the Cheshire Cat as:
““If you don’t know where you are going, then any road will take you there.””
Alignment between business and IT is critical, ensuring the road you choose to take is the one that will take you where you want to go, and a robust data strategy is a map to guide you on that journey via the right road.