Anyone who believes that a well-conceived, skillfully executed analytics strategy has its place somewhere in the distant future of their enterprise needs to rethink this assumption.

Analytics cannot be thought of as something that we need to worry about next year or next quarter, especially since organizations are now confronted with greater volumes of data than ever before. We need to get these programs going now. Those who chose to wait will lose; it is really that simple. 

Why the urgency? Unfortunately, there is no easy answer. Industry experts opine on how everything is being driven by increasingly sophisticated, readily accessible technologies. The most commonly cited examples include mobile devices (tablets, smartphones, media players, etc.), ubiquitous broadband access to the Web (enabling rich multimedia experiences), social media (giving practically everyone a voice), “big data,” unstructured data and the consumerization of technology (bring your own tech to work), to name a few. These factors are not incorrectly attributed, but they only explain part of the story.   

The other part of the story is best understood as a cultural shift that is happening quietly around us, every minute of every day. This shift is certainly fueled by these and other technologies, but is better characterized by some fundamental changes in our attitudes toward ourselves and each other, our values and beliefs, the way we work and play, the way we communicate, the way we buy things and, most importantly, the way we consume goods and services. And that is what the urgency is all about. It can be daunting, even overwhelming, or it can be exciting and highly rewarding. Our world is filled with all sizes, shapes and colors of new business and personal opportunity everywhere we look. The urgent need revolves around taking advantage of these opportunities and getting it right.

According to a recent study performed by IBM’s Institute for Business Value, people and companies create 2.5 quintillion characters of data every day.  The study further asserts that 90 percent of the data in the world today was created in the last two years.  We truly have arrived at the era of big data and big data analytics. Let’s look at a few facts that we can relate to:

  • Facebook has more 800 million registered users. This is approximately 10 percent of world’s population.
  • Fifty percent of active Facebook users log on every day.
  • The average Facebook user has 130 friends.
  • Facebook users install 20 million applications per day on the network.
  • 250 million people connect to Facebook from external websites every month.
  • More than 2.5 million websites are integrated with Facebook, including 80 of comScore’s U.S. top 100 websites and more than 50 of comScore’s global top 200 websites.

On Facebook during an average 20-minute period:

  • > 1 million links are shared.
  • 1.3 million photos are tagged.
  • 1.5 million invites are sent.
  • 1.6 million wall posts are published.
  • 1.9 million status updates are published.
  • 2.0 million friend requests are accepted.
  • 2.7 million photos are uploaded.
  • 4.6 million messages are sent.
  • 10.2 million comments are shared.

One can only imagine the economic value of the actionable customer insights embedded in this data, and this is just the data attributed to Facebook. Add to this gold mine information about customer purchases (online and terrestrial), cell phone content, email content, geospatial information, search engine usage, websites visited, etc. We are living in a world that is rich with “digital exhaust” and it is everywhere. It is clear that individuals and enterprises that can “get it right” will have a competitive advantage over their peers who are deferring the planning and execution of their customer analytics strategy because it is big, complex and expensive. They are simply not considering the even bigger expense associated with waiting — extinction.
There are numerous examples to consider. In financial services, firms are employing big data analytics to optimize their operational efficiency. The increasing volumes and frequency of data are changing the way that companies think about analyzing their data (e.g., throw away 90 percent; analyze 10 percent). Big data analytics necessitates new technology, processes and skills to enable firms to focus on the “art of the possible.” Organizations need to employ change management practices to adapt to the opportunities resulting from the volume, velocity and variety of data available for analysis. In the life sciences, analysis of big data sets is enabling breakthroughs in genomics and bioinformatics.

Eric Perakslis, CIO and chief scientist for the Food and Drug Administration, recently noted that informatics is 80 percent data and 20 percent systems. Professor Tom Davenport, author of the analytics bible “Competing on Analytics,” reminds us of the essential truth about the value of analytics when he wrote in an HBR blog post that even “small data can improve your organization’s judgment.” Lastly, organizations have been able to employ analytics in sandbox environments to perform rapid analyses that dramatically accelerate time-to-answer and the process of moving from data to decision. Thus, businesses, consumer products firms, media companies, scientists and government agencies are able to react quickly and make decisions with greater speed and information. 

Now that the case for investing in big data analytics is made, we must ask: Where does one begin? And what does one do next? Here are a few guiding principles to get started:

  • Recognize that the enactment of any analytics strategy is a continuous journey, not a discrete deliverable.
  • Think big but act in small, incremental steps that produce tangible business value along the way.
  • Focus simultaneously upon both business outcomes (e.g., increased revenue, market penetration, wallet share, etc.) and data available (e.g., sources, content, veracity, time-value, etc.,). Focusing on outcomes alone will result in analysis paralysis; focusing on data only will be like trying to learn a language by reading the dictionary.
  • Do not accept the notion that if we build it, they will come. For example, warehousing data, with no clear path to actionable insight or business value creation, simply to have for a rainy day will only waste money.
  • Consider that as the world becomes more and more interconnected, the time from data collection to acting upon insights derived will become shorter and shorter. We no longer live in a world where looking at 12 rolling months of historical data will help predict what to do during the following 12 months.
  • It’s all about delighting the customer (external or internal) by providing the best possible experience based upon who the customer is and their behavior, as opposed to what segment the customer fits into and how you think that segment should behave.
  • Reevaluate your segmentation and go-to-market strategies based on all of the above.  While reaching a certain demographic (e.g., males between the ages of 15 and 24) was revolutionary in the 20th century, knowing that Bob Smith and his wife Mary just gave birth to their first child and are now in their car about a quarter of a mile from your infant boutique will provide you with a perfect opportunity to send them a text message of the location of your store and a discount coupon attached.

The IBM Institute for Business Value study presented a four-part capability framework for designing and implementing an analytics strategy based upon current enterprise positioning and readiness to embrace the methods and procedures found to be most successful within top-performing companies. The framework is presented in two dimensions, arranged in quadrants. One dimension helps companies understand what is needed to move from internally focused, push-based marketing to the externally focused, pull-based use of digital customer analytics. The other dimension addresses the capabilities needed to move from sharing information internally and across the value chain to creating business models that enable faster value creation. 

In the end, any successful analytics strategy will depend upon the design and institutionalization of a governance program to guide the journey. Governance ensures:

  1. Clear articulation and communication of the digital customer strategy and roadmap to all stakeholders involved.
  2. Executive-level business sponsorship and ongoing involvement.
  3. Committed collaboration with internal and external IT partners and information providers.
  4. Agreement upon appropriate business-driven outcomes and the articulation of a metrics-based dashboard to measure and communicate progress.
  5. A straightforward end-to-end process to evaluate, select, prioritize and fund proposed initiatives and to effectively measure return on investments, comparing these with expected business outcomes.
  6. Flexibility to adapt goals, objectives and priorities to current economic conditions, while remaining sensitive to the enterprise’s position within the marketplace.

We all see the benefits that big data analytics will provide for the 21st century enterprise. The concepts discussed here will arm you with fundamentals for success in that journey.

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