2 Factors Contribute to Successful Data Analytics

2 DATA FACTORS

In today’s corporate environment data is readily available in many forms that range from business transactions such as sales, help desk tickets, customer surveys or feedback, and web traffic. In the past, many organizations sought to gain insights from the data to simply validate sales efforts or expand to new regions. To investigate the potential behind the wealth of data organizations have to delve into two factors that enable them to properly focus on data analytics as the logical next step to being competitive in today’s markets.

Leveraging Big Data Today

Generally, many organizations are at a loss for how to leverage big data. Big data essentially being the enormous amounts of data generated and harvested by organizations today (Mithas, Lee, Earley, Murugesan & Djavanshir, 2013). Analyzing data can enable business organizations to transcend business value and reach the realm of competitive advantage (Kiron & Shockley, 2011).

Unfortunately organizations are not always able to capitalize on the literal gold mine of data. An MIT Sloan study found 58% of business respondents claimed to have gained competitive advantage through the use of data analytics. One caveat though was that the competitive advantage was only gained by companies that were already leveraging data analytics (Kiron & Shockley). Thus, some businesses may begin analyzing their data and not capitalize on it. Which means there are certain factors upon which analytical success hinges.

Data Strategy and Data Culture as Factors for Analytics Success

So what factors ensure a payoff in terms of business value much less competitive advantage for an organization deciding to analyze their data? Based on my experience and research, I surmise that there are two factors.

Factor 1: Before organizations embark on analytical endeavors, it is imperative that they strategically layout achievable goals with well-defined outcomes and business cases to achieve sufficient value to justify ongoing data analyses (Mithas et al., 2013). In my experience, I have found that many organizations that approach analytics as a one time opportunity to dig through a plethora of data often fail to find much value. To this end, I suggest that organizations avoid opportunistic research endeavors in the hopes of findings something tangible to pursue. In other words, don’t search through data in the hopes of finding a gem. Instead establish a strategy that clearly defines what you want to pursue.

Factor 2: In addition to strategy, organizations that are most likely to find success are those that embrace a data oriented culture. A healthy data oriented culture that supports the use of analytics throughout the organization is likely to innovatively tackle strategic organizational threats (Kiron & Shockley, 2011). This essentially means that there must be a data oriented mindset and willingness to work through data to find value. I personally have found that organizations that adopt a good analytic habits eventually realize the benefit of a data oriented mindset. The reason is, that the analyst find flakes of gold along the way and when they follow the crumbs long enough they can see where the true nuggets reside. Analytics cannot be a one-time flash in the pan, but rather a series of searches that reveal value over time.

Two Factor Model

It is clear that using big data can pay off either in terms of simple business value and/or competitive advantage. However, the approach to operationalizing data analytic endeavors must be dual faceted. Thus I created this two factor model. The model depicts that a move toward a rigidly defined data strategy and a move toward a strong data culture can result in opportunities for increased business value.

Conclusion

Organizations can continue to tackle big data analytics wantonly or take a more strategic approach that will serve to guide them in the right direction as well as strengthen their culture. There are actions organizations can take to achieve increased business value with data analytics. Future posts on this topic will follow.

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