Data has exploded in a way that rivals mobile’s explosion ten years ago. Everyone is out there buying masters degrees, data visualization licenses, and data scientists by the truck loads in a way that mimics corporations buying mac laptops, mobile developers, and app store branding when iPhones blew up the smart phone space.

The Analytics ‘Trend’ Isn’t New

There are a lot of great things taking place right now with all the interest around data analysis, but the funny thing is that data analysis is nothing new (neither is data science). There’s a good 30-40 years of work on data, from data architecture to database administration (not to mention the millions of excel spreadsheets that corporations are running critical business functions on) that live inside companies and create a legacy layer that this latest wave of data analysis is building on.

Other new trends, such as big data analysis and the cloud computing revolution, have further spurred companies to consider ways to extract usefulness from their existing data and move away from churn or ARPU and develop distinctly competitive analysis with phrases like “regression analysis” and “predictive analytics” becoming much more common in corporate board rooms.

Translating Data

The big problem is, as was the case with mobile, is that you have to be able to translate interesting technology into impacting ROI-laden investments that drive top or bottom line revenues (or create efficiency and lower costs of course, as well). There’s a good deal of buzz around big data being an overused term, and a hundreds of millions of dollars spent on visualization tools will, at some point, taper off when the average business user turned dashboard builder runs out of things to visualize due to saturation, bad data, etc.

So Who / What Is This Chief Analytics Officer?

A Chief Analytics Officer could be a Director of Data, or a VP of Analytics, but having someone at an executive level that can drive a centralized data strategy for the company should exist for these three reasons.

  1. Centralizing Your Data Resources Will Help Avoid Silo’ed Capabilities

To turn all this hype into profit, it means building a centralized capacity. A capacity which sites outside of the IT-to-business politics and hype to buy visualization tools, and instead focusing on building a stack of capabilities, from the data lake to the dashboards, geared around revenue generating use cases taken from business partners who need more usefulness from their data without having to build silo’ed data science teams that rely on fractured data sets.

When anything is this pumped up, every department is going to want to get involved and build capabilities, since every business group uses data in some form or another. The problem is that it takes a variety of experiences and backgrounds, along with investments, that need to be built at a corporate level with a plan to centralize some capabilities and decentralize others with a clear data strategy that everyone can get behind.

Centralizing this capability means one strategy, one leader, and limitless opportunities for everyone to participate without each department deciding their own game plan for riding this data wave.

  1. Consolidating data to maximize usefulness, while aligning that effort under a single leader

The topics around big data, and data lakes are growing overwhelming, with more and more companies working to consolidate all their data in one place to allow for both advanced analytics & traditional business intelligence functions. At the same time, a data lake built in the wrong way can cause latency along with too many executive peers building extensive requirements which ultimately brings any progress to a halt.

Bringing your data consolidation effort under a single leader, tied to a data strategy that brings the bigger outcomes into focus and alignment while leaving the smaller day to day details up to a single org unit means your company can spend less time planning & debating, and more time driving value from your data lake.

  1. Impact is prioritized, over ‘interesting trends’

Much like the millions of dollars spent on corporate mobile apps that never got traction, companies today are spending millions of dollars on real time streaming, data visualization, and corporate education on DAX programming all in an attempt to capitalize on the data analytics hype and create a stronger bottom and/or top line revenue stream through the use of data analysis.

The thing is, data isn’t a new domain for technology, nor is investing in Big data going to revolutionize your company.

There’s a good deal of effort being spent on building impressive looking visuals, which add no incremental value over the same data displayed in an excel chart. Furthermore, companies investing in hiring legions of data scientists without clear revenue-driving hypothesis will find they spend a good deal of time figuring out just what to focus on.

As is the case with any over-hyped technology, whether it’s enterprise wide tableau licensing or infrastructure to support web traffic analysis for real time personalization, the tools are only as good as the capabilities on the team and the business cases they are actively working towards.

Focusing on a single leadership structure to come up with the real tangible value for investment in data analytics means there’s a common set of goals that’s driving the spend, and a clear idea of what each department and employee is focusing on.

It’s not so much that a single team owns every analyst, but rather each instrument is calibrated so the whole company sounds like a beautiful concerto vs a number of instruments playing at different rhythms.

Furthermore, when it comes to the vendor onslaught and procurement nightmares that naturally arise in the midst of a technology boom, there’s a clear investment strategy for how the company plans to leverage capabilities such as big data or advanced analytics. This can influence everything from recruiting and training, to infrastructure and software licensing, and help ensure each investment is additive vs expensive and lacking in impact.

There’s a good deal of interesting happenings in the data space right now, but companies need more impact to back up the cost.

There are no doubt other benefits I’ve missed out on taking data seriously, and putting someone in charge who is somewhat removed from the politics and inefficiencies that come from burying the capability inside an existing org (similar to the CIO coming of age, and now no longer reporting to CFOs in most companies).

The aim is however, to ensure your data analytics efforts are making a meaningful impact, and driving the kinds of returns most companies never experienced during the mobile app boom almost ten years ago now. And in so doing, benefiting every company that invests in the great capabilities a data-driven org has at its disposal.

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