5 Predictions About Analytics, 4 Tips to Get Started & 3 Cautionary Thoughts

This is the time of year for predictions and there is no shortage of them in the analytics arena. As business owners and managers are redoubling their efforts to find competitive differentiation amid tepid growth projections for 2011, analytics is seen by many leaders as a way to gain an edge.

There are a few key predictions that are shared by seasoned analytics champions and neophytes alike:

• Organizational data is proliferating at an alarming rate, both in terms of volume and complexity. How to make sense of all of this data will be a challenge for those not on the analytics bullet train.

• Desktop analytics will dominate the business environment, making large servers and high cost analytic languages no longer able to return the desired ROI.

• Mobile applications will be hot topics. Devices like iPads, smart phones and tablets will bring analytics into end users’ hands like never before.

• The gap between heavy analytics users and laggards will continue to widen and it will become apparent in areas like innovation and product development as well as bottom line results.

• Privacy regulations could make the collection of personal data more restrictive. At the same time, individuals may balk at the idea of how much of their private information is in the hands of third parties.

Michael Lock of the Aberdeen Group and Caroline Seymour of IBM’s Mid-Size Business unit have some helpful pointers for companies that are taking their first steps into business analytics:

Get Control of Your Data: This means bringing disparate buckets of data into a consistent environment so it’s easier for more people to perform multi-dimensional analysis.

Analyze Data in a Business Context: Data analysis in isolation provides no insight and therefore has limited value to the business. Analytics works for the organization when there is a business strategy to address outside pressures, an assessment of capabilities and analytical needs and the ability to use analytics across the organization.

Think Big – Start Small: This is what Michael Lock calls the Land and Expand strategy. Start with one unit or one pain point and work up to the enterprise level of data consistency. Match resources to the company’s budget.

Empower Non-technical Users: 77% of the Best-in-Class companies measured by Aberdeen Group have what they call “pervasive Business Intelligence with self-service usage”. Only 10% of the Laggards have it. End users have the business knowledge, the business context and the ability to create insight from data.

I’ve been involved in so many fads du jour, from reengineering to knowledge management. All of the concepts were stellar but became hijacked by (gasp!) consultants selling technology or off shoring services or some effort to gain short-term advantages. The problem seemed to be either that the ROI assumptions were inaccurate or that consultants rarely stayed around to see the business through the painful change that inevitably comes with disruptive innovations.

Now for the words of caution...

Leaders Drive Change. That’s what GE’s CEO Jeff Emmelt says and I believe him.

Culture Trumps Strategy. Becoming an analytics-based business means behaviors change across the board. This is often left off the To-Do list.

The Collective Mindset Needs to Shift. If data is a source of power in the organization; if people think they’ve been successful making “gut” decisions; if collaboration isn’t in your vocabulary, you have some work to do to build a successful analytics-based company. But, the rewards are going to be huge.

Three Big Trends That Will Change the Way You Make Decisions

I attended a seminar this week on predictive analytics, a topic some say would cure insomnia. But, I found the trends important and worth more consideration by anyone who owns a business or runs one or is employed by one – so the majority of us.

I love data, even as a totally right-brained person, because it has a story to tell. The problem is we’ve exhausted the process of using lagging indicators to produce insight about future decisions. Companies should be moving from silos of data hoarded and rarely aggregated to a point where employees collaborate and make real time, fact-based decisions based on modeling organizational data and assessing the power of one choice over others to achieve results.

A few years ago, Thomas Davenport wrote a book titled, Competing on Analytics and cited large companies such as Marriott, Harrah’s and Progressive Insurance as the analytics champions. Not much hope for the rest of us, is that what you’re thinking?

Here’s what I learned from that seminar and I believe it is important for businesses of all sizes to get really clear about the implications of these trends:

Analytics are moving downstream. What was once done by a cube farm full of PhD’s will be done by us regular people who are tasked to come up with hard evidence for what we do (market, train, deploy technology, in short, everything). Technology will make it possible to collaborate with other functions to aggregate data and perform our own statistical and predictive work. On our laptops. In real time. Maybe a lone PhD floating among us.

Analytics are moving into every function. No longer will we be able to get by with a "I -can’t- quantify- the- ROI -of –why- I –need- this- money- from- the- budget-but- trust-me- on- this". Jack Fitz-Enz said it best in his new book The New HR Analytics: if the HR department doesn’t feel up to handling human capital issues in a quantifiable, predictive way; the C-suite will give the responsibility to someone else. That holds true for every function from Marketing to Customer Service.

Predictive analytics are a competitive advantage. At a time when we all are looking for the Holy Grail of business success, if your company isn’t starting now to explore the concept, it could find itself out-maneuvered and shut out by the competition.

• What if your competition could predict which of its customers was likely to defect in 6 months and offer them a sweetheart deal before they are out the door?
• How much money will you spend trying to woo a customer that isn’t interested in moving her business to you because you don’t know which behaviors trigger a purchase?
• What if you could predict which employees had the greatest power to impact customer loyalty and could increase the likelihood of retaining them by customizing their rewards and recognition?

Am I going to turn away from my intuition or sense of what feels right in favor of analytics alone? Heck no, but using both is the right equation: Intuition+ Experience + Analytics = Insight + Results.

How about you?