IT Is All About Data!
Is this how you think about IT? Or do you think of IT in terms of the technology it deploys instead? I recently was interviewing a CIO at Fortune 50 Company about the changing role of CIOs. When I asked him about which technology issues were most important, this CIO said something that surprised me.
He said, “IT is all about data. Think about it. What we do in IT is all about the intake of data, the processing of data, the store of data, and the analyzing of data. And we need, from data, to increasingly provide the intelligence to make better decisions”.
How many view the function of the IT organization with such clarity?
This was the question that I had after hearing this CIO. And how many IT organizations view IT as really a data system? It must not be very many. Jeanne Ross from MIT CISR contends in her book that company data “one of its most important assets, is patchy, error-prone, and not up-to-date” (Enterprise Architecture as Strategy, Jeanne Ross, page 7). Jeanne contends as well that companies having a data centric view “have higher profitability, experience faster time to market, and get more value from their IT investments” (Enterprise Architecture as Strategy, Jeanne Ross, page 2).
What then do you need to do to get your data house in order?
What then should IT organizations do to move their data from something that is “patchy, error-prone, and not up-to-date” to something that is trustworthy and timely? I would contend our CIO friend had it right. We need to manage all four elements of our data process better.
1. Input Data Correctly
You need start by making sure that the data you produced is done so consistently and correctly. I liken the need here to a problem that I had with my electronic bill pay a few years ago. My bank when it changed bill payment service providers started sending my payments to a set of out of date payee addresses. This caused me to receive late fees and for my credit score to actually go down. The same kind of thing can happen to a business when there are duplicate customers or customer addresses are entered incorrectly. So much of marketing today is about increasing customer intimacy. It is hard to improve customer intimacy when you bug the same customer too much or never connect with a customer because you had a bad address for them.
2. Process Data to Produce Meaningful Results
You need to collect and manipulate data to derive meaningful information. This is largely about processing data so it results produce meaningful analysis. To do this well, you need to take out the data quality issues from the data that is produced. We want, in this step, to make data is “trustworthy” to business users.
With this, data can be consolidated into a single view of customer, financial account, etc. A CFO explained the importance of this step by saying the following:
“We often have redundancies in each system and within the chart of accounts the names and numbers can differ from system to system. And as you establish a bigger and bigger set of systems, you need to, in accounting parlance, to roll-up the charter of accounts”.
Once data is consistently put together, then you need to consolidate it so that it can be used by business users. This means that aggregates need to be created for business analysis. These should support dimensional analysis so that business users can truly answer why something happened. For finance organizations, timely aggregated data with supporting dimensional analysis enables them to establish themselves as “a business person versus a bean counting historically oriented CPA”. Having this data answers questions like the following:
- Why are sales not being achieved? Which regions or products are failing to be delivered?
- Or why is the projected income statement not in conformance with plan? Which expense categories should be we cut in order to ensure the income statement is in line with business expectation?
3. Store Data Where it is Most Appropriate
Data storage needs to be able to occur today in many ways. It can be in applications, a data warehouse, or even, a Hadoop cluster. You need here to have an overriding data architecture that considers the entire lifecycle of data. A key element of doing this well involves archiving data as it becomes inactive and protecting data across its entire lifecycle. The former can involve as well the disposing of information. And the latter requires the ability to audit, block, and dynamically mask sensitive production data to prevent unauthorized access.
4. Enable analysis including the discovery, testing, and putting of data together
Analysis today is not just about the analysis tools. It is about enabling users to discover, test, and put data together. CFOs that we have talked to say they want analysis to expose earlier potential business problems. They want, for example, to know about metrics like average selling price and gross margins by account or by product. They want as well to see when they have seasonality affects.
Increasingly, CFOs need to use this information to help predict what the future of their business will look like. CFOs say that they want at the same time to help their businesses make better decisions from data. Limiting them today from doing this are disparate systems that cannot talk to each other. CFOs complain about their enterprise hodgepodge of systems that do not talk to one another. And yet to report, CFOs need to traverse between front office to back office systems.
One CIO said to us that the end of any analysis layer should be the ability to trust data and make dependable business decisions. And once dependable data exists, business users say that they want “access to data when they need it. They want to get data when and where you need it”. One CIO likened what is need here to orchestration when he said:
“Users want to be able to self-service. They want to be able to assembly data and put it together and do it from different sources at different times. I want them to be able to have no preconceived process. I want them to be able discover data across all sources”.
So as we said at the beginning of this post, IT is all about the data. And with mobile systems of engagement, IT’s customers are wanting increasingly their data at their fingertips. This means that business users need to be able to trust that the data they use for business analysis is timely and accurate. This demands that IT organizations get better at managing their core function—data.
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