What do most CIOs view as their top business priority? Establishing stronger business and IT alignment. One area where CIOs need to ensure better alignment is with enterprise analytics. CIOs have told me business users are demanding ubiquitous computing, where they can get their data on any device when it is needed.
For this reason, even though CIOs are interested in the mechanisms of data delivery and data defense — data integration, data cleanliness, data governance, data mastering and even metadata management — they would not take a meeting on these topics in isolation of their business consequences. CIOs will get their business partners involved here — increasingly, this includes a chief data officer (CDO). CIOs these days require a business value proposition for everything they expend resources and investments on. Given this, CIOs want to hear about what the business wants to hear about.
- Enabling new business insights to happen faster.
- Moving from reporting to real-time management by observation.
- Enabling their businesses to automate and improve customer-facing processes.
Where CIOs Need to Focus to Improve Analytics Capabilities
What role does the CIO have in setting the agenda when a business wants to compete with analytics? Tom Davenport said CIOs have good intentions when it comes to developing an enterprise information strategy, something I've heard echoed in my own conversations. They can see the value of taking an enterprise-wide view of analytics. However, Davenport suggested CIOs should start by focusing on the analytics that matter most to the business. He also recommended IT organizations build an IT infrastructure capable of delivering the information and analytics that people across the enterprise need now and in the future.
To me, this is increasingly about enabling automation and predictive modeling.
Achieving this involves establishing a single data platform that includes end-to-end data pipelines. In "Competing in the Age of AI," Marco Iansiti and Karim Lakhani note the value of the massive amount of data captured by users, suppliers, partners and employees. For this reason they argue the data should not be stored in ad hoc fashion but instead needs data governance and security. To accomplish this, they suggest organizations build a secure, centralized system for data security and governance, define appropriate checks and balances on access and security, inventory assets, and provide all users the necessary protections. A key element of the data platform is the concept of a data pipeline, which completes several data functions such as gathering, inputting, cleaning, integrating and safeguarding data in a systematic, sustainable and scalable way.
At the same time, Davenport said IT organizations need to resist the temptation to provide analytics as an add-on or on a bolt-on basis for whatever transaction systems have just been developed. As a product manager, I had a development team that preferred to add analytics by source rather than do the hard work of creating integrative measures that crossed sources. So I have firsthand experience with this problem.
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IT as Part Owners of Analytics
In the early stages, IT organizations need to focus more on a self-service analytics approach. But as the business's analytics capabilities mature, Davenport said IT needs to shift gears and become a proactive advocate and architect of change, advocating for IT to be a part owner of the company’s analytical capabilities.
IT managers, therefore, must understand and be able to articulate the potential for analytics being created at an enterprise level. At the same time, the IT staff — which often lacks the heavy mathematical background of data scientists — needs to be able to interact with the analytics pros who use and consume the information that IT creates to build models. I experienced such a dilemma when my analytics modelers were disconnected from the data lake and BI developers. They were two different communities working on a common project, and although some modelers could build apps or even a BI system, what excited them most was building new analytical models.
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IT Should Be Talking the Language of Business
Davenport said by discussing decision-making, insights and business performance instead of discussing cloud computing, service-oriented architecture, or even OLAP, IT managers can improve and make their interactions with the business and analysts easier. Meanwhile, Davenport believes the enterprise analytics journey starts with good, integrated data on transactions and business processes managed through enterprise applications like ERP and CRM.
Focusing on the Big Questions and the Right Problems
Driving the business to focus on the big questions and the right problems is critical, and IT can facilitate this. Why does it matter? A 2007 Accenture study found that “companies that derived any real value from their analytics anticipated how to leverage the information to generate new insights to improve business performance." Unfortunately, to this day, too few organizations take this necessary step.
However, if you've successfully accomplished this step, the next task is to eliminate legacy BI systems and old spaghetti code as well as silo data marts. The aim should be to replace them with an enterprise analytics capability that answers the big questions. This requires standardization around an enterprise-wide approach that ensures a consistent approach to data management and provides an integrated environment complete with data repositories/data lakes, analytical tools, presentation applications and transformational tools. This investment should be focused on improving business processes and increasingly so-called integrated experiences. IT’s job is to watch out for current and future users of information systems, according to Davenport.
What is your IT organization up to today? Is it focused on solving the right problems with data? According to Iansiti and Lakhani, digital disruptors are using data and analytics not only to transform business models, but to erase your advantages in scale and scope. It's time you invest in building your AI factory, and, in particular, your data pipeline. Only by becoming an AI company do you have chance of maintaining relevance in an increasingly digital era.