Why Organizations Fail To Adopt Data Analytics

Despite all the talk about how data insights can impact the bottom line, many organizations are still laggards at data analytics. The disparity is probably most obvious in the retail industry, where the competitors of retail juggernauts such as Amazon and Walmart are still harnessing relatively basic tools.

To understand why organizations fail to adopt advanced data analytics, the Harvard Business Review (HBR) interviewed a score of global retail executives from the Americas, Europe, and Asia. From speaking with a total of 24 business leaders, they narrowed down six obstacles to the adoption of data analytics.

I outline a trio of them that are relevant to non-retail industries – and highlight what organizations can do about it.

Culture and people

The first barrier when it comes to analytics is cultural. Specifically, many companies struggle with risk aversion and lack defined objectives. And while everyone might acknowledge its importance, most are unable to articulate why when pressed. What’s more, some individuals perceive analytics as more of an art form than a scientific discipline, resulting in a dismissive attitude.

This becomes entrenched when the analytics function is run by people who do not fully understand the business. Chances are high that the analytics will inevitably offer an erroneous observation or impractical recommendation to problem owners, culminating with unfavorable impressions that result in them no longer taking the person seriously.

A lack of data talent is another challenge. But while the focus is typically on getting employees who can design and use analytics tools, HBR suggests that what businesses need most are employees who can bridge the gap between analytics and the business.

This recommendation mirrors that of a senior director of data and analytics to CDOTrends previously, who said: “[We need] to identify a data science evangelist who can act as the ‘bridge’ between business functions to the data science team. This data science ‘translator’ is someone who understands the business context and can identify the right data science solution.”

Data quality and management

Finally, data quality and data management were cited as the biggest problems. Data is either siloed in various places around the organization or managed haphazardly. Moreover, important data needed by the business might not even be collected at the moment, necessitating the implementation of costly new solutions to generate the needed data.

To be clear, executives want higher-quality data and smarter machine-learning tools to support them with demand planning, modeling, and solution strategies. But there is no getting there without first decluttering their data and breaking down data silos to establish a single source of truth.

The breaking down of data silos is vital. As I wrote last year, the very first step is to gain visibility over all the data in the organization. Only then do organizations have a hope of organizing it, cleaning it, and setting up the systems to keep it that way.

Doing something about it

The way forward calls for organizational redesign and strategic investments, according to HBR. The organizations that came out ahead tend to follow the mantra of “Think big, start small, and scale fast”.

“Leaders can spearhead an internal campaign emphasizing that analytics are meant to empower decision-makers, not replace them. Foster a culture whereby employees are rewarded for understanding the predictions and prescriptions generated by analytical tools instead of merely executing the recommendations and rewarding compliance.”

There is no question that substantial investments are required, especially the replacement of legacy systems with cloud-based systems. Cloud systems, it would appear, can be more easily designed to scale and utilize the growing availability of big data.

And yes, the report recommends businesses invest in data talents and develop a pipeline for such talents. This could be achieved by collaborating with universities offering data science degrees or something similar, or by developing training programs for existing employees.

You can read the article titled “Why retailers fail to adopt advanced data analytics” here.

Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at [email protected].​

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