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Data Analytics Mistakes that Successful Retailers Avoid
6th January 2019
Data Analytics mistakes retailers should avoid

The meteoric rise of social media and the ability to track, correlate and capture large amounts of data have resulted in the widespread adoption of Big Data analytics by businesses eager to get a handle on emerging trends and customer behavior. The retail industry, in particular, has invested heavily in the strategy and enthusiastically incorporated the insights – in lead generation, customizing customer experience and incorporating customer feedback, among other outcomes. However, despite the general advantages that all businesses can gain from data analytics, avoiding a few pitfalls is essential.

Despite the enormous amounts of available customer data and extensive interpretation capacities, numerous studies show that retail businesses have yet to gain the optimal benefit of big data analytics. According to the McKinsey Global Institute commissioned, ‘The Age of Analytics’ report, the U.S. retail industry has realized less than 40 percent of the projected value of the big data it has access to.

Big data is a tool for insights, commitment to transformation is the real key

Several advanced tools are available to the modern organization to implement a successful big data driven transformation. Microsoft Dynamics 365 for Customer Insights is a cloud-based SaaS solution that allows the collation of data gathered from multiple sources, in order to generate actionable insights. However, as initiatives go, deriving value from data analytics is more about committing to a cultural shift than processing data and arriving at conclusions.  While technology and customer behavior has created the ideal scenario for such initiatives, unfocused and inadequate data analytics can potentially be worse than none at all.

Avoiding the common mistakes in implementing a big data driven transformation

It seems only a few years ago that the primary challenge to be overcome in successfully implementing a big data analytics initiative was convincing CXOs to invest in it. Successful implementation by industry leaders has created a convincing argument for the strategy, since then. However, the key to deriving truly transformational benefit has other challenges to overcome. The following are some shortcomings to avoid, that can make the difference between outstanding success and underwhelming returns:

  • Non specific implementation: One size fits all solutions are inadequate by definition. The nature of your specific business, your customer base, product profiles and industry specifics, should all feature prominently in the equation, when implementing an effective program.
  • Undefined metrics: Data needs to be contextualized for it to generate insights. For a data analytics initiative to succeed, it needs to be driven by comprehensive metrics that define the data itself, as well as the transformation goals your organization is pursuing.
  • Creation of silos: All organizations have divisions and departments that are geared towards superficially divergent goals. The pursuit of division specific outcomes can organically generate silos that detract from larger goals and impede organizations in identifying transformation opportunities.
  • Lack of leadership: Leadership is a critical element in the success or failure of a data driven transformation. It is usually top management that has the bird’s eye view that should inform analysis. In addition, transformation needs to be marked with a cultural shift, that is best propagated top-down.
  • Deterministic analysis: Enterprises are an organic entity, rather than a mechanistic one. Analysis should reflect this. Arriving at decisions, as opposed to deterministic interpretations, is the key to achieving truly transformative change.

Conclusion

Effective big data analytics is a fusion of right and left brain approaches. Identifying crucial data sets, interpreting the trends they indicate and generating insights that reflect the larger goals of the organization is crucial. This can only happen effectively if the data gathered, its context and the insights drawn are aligned, balanced and cohesive.

At Levtech, we provide data analytics services such as BI Consulting, Data Visualization, and Data Science. Read more about these services here,  and to understand how Levtech can help you to gain deep insights into your business,  please reach out to us at marketing@levtechconsulting.com.   

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