Businesses are using analytics to stay competitive. One by one, departments are moving from static reports to modern analytics so they can fine-tune their operations. There’s no shortage of solutions designed for specific functions, such as marketing, sales, customer service and supply chain, most of which are available in SaaS form. So, when it’s possible just to pull out a credit card and get started with an application, why complicate things by involving IT?
Freedom from IT seems like a liberating concept until something goes wrong. When data isn’t available or the software doesn’t work as advertised, it becomes the IT department’s job to fix it.
“I used to call this the BI mid-life crisis. Usually about a year and a half or two years in, [departments] realize they can’t report accurately and then they need some help,” said Jen Underwood, founder of Impact Analytix, and a recognized analytics industry expert. “Now I’m seeing more IT involvement again.”
Organizations serious about competing on insights need to think holistically about how they’re approaching analytics and the role of IT. Disenfranchising IT from analytics may prove to be short-sighted. For example, a proof of concept may not scale well or the data required to answer a question might not be available.
Analytics’ long-term success depends on IT
IT was once the sole gatekeeper of technology, but as the pace of business has continued to accelerate, departments have become less tolerant of delays caused by IT. While it’s true no one understands departmental requirements better than the department itself, IT is better equipped to identify what could go wrong, technically speaking.
Even if a department owns and manages all of its data, at some point it will likely want to combine that data with other data, perhaps from a different group.
“We became accustomed to IT organizations managing the database architectures or the data stores and any of the enterprise wide user-facing applications,” said Steven Escaravage, vice president in Booz Allen Hamilton’sStrategic Innovation Group. “I think that’s changed over the last decade, where there’s been a greater focus on data governance, and so you also see IT organizations today managing the process and the systems used to govern data.”
Additionally, as more organizations start analyzing cross-functional data, it becomes apparent that the IT function is necessary.
“IT plays an important part in ensuring that these new and different kinds of data are in a platform or connected or integrated in a way that the business can use. That is the most important thing and something companies struggle with,” said Justin Honaman, a managing director in the Digital Technology Advisory at Accenture.
Where analytics talent resides varies greatly
There’s an ongoing debate about where analytics talent should reside in a business unit. It’s common for departments to have their own business analysts, but data science teams, including data analysts, often reside in IT.
The argument in favor of a centralized analyst team is visibility across the organization, though domain-specific knowledge can be a problem. The argument in favor of decentralization is the reverse. Accenture’s Honoman said he’s seeing more adoption of the decentralized model in large companies.
Hybrid analytics teams, like hybrid IT, combines a center of excellence with dedicated departmental resources.
Hot analytics techs
Machine learning and AI are becoming popular features of analytics solutions. However, letting machine learning loose on dirty and biased data can lead to spurious results; the value of predictive and prescriptive analytics depends on their accuracy.
As machine learning-based applications become more in vogue, analytics success depends on “the quality of not just the data, but the metadata associated with it [that] we can use for tagging and annotation,” said Booz Allen Hamilton’s Escaravage “If IT is not handling all of that themselves, they’re insisting that groups have metadata management and data management capabilities.”
Meanwhile, the IoT is complicating IT ecosystems by adding more devices and edge analytics to the mix. Edge analytics ensures that the enterprise can filter meaningful data out of the mind-boggling amount of data IoT devices can collect and generate.
In short, the analytical maturity of organizations can’t advance without IT’s involvement.
Just a Bit of Advice:
Strategies for Successful Analytics
A few helpful hints as you move through your analytics journey.
If you’re just getting started on your data and analytics journey, think before you act.
Steven Escaravage of Booz Allen Hamilton noted, “I tell clients to take a step back before they invest millions of dollars.” Among other things, he said, make sure to have a good foundation around what questions you’re trying to solve today and the questions you perceive are coming down the path.
“Let’s put together a data wish list and compare it to where we’re at, because usually you’re going to have to make investments in generating data to answer questions effectively,” he added. All the other pieces about methods and techniques, tools and solutions follow these actions.
If you’re at the pilot stage, beware of scalability challenges.
“Very rarely for sophisticated analytic problems would I lean on a typical Python pilot deployment in production,” said Escaravage. “You’d typically move to something you knew could scale and wouldn’t become a bottleneck in the computational pipeline.”
If you’re in production, you may be analyzing all kinds of things, but are you measuring the effectiveness of your solutions, processes and outcomes? If not, you may not have the complete feedback loop you think you have.