5 Challenges in Implementing Big Data Analytics Based on Our Client’s Experience

While well-informed decision-making makes all the difference, it comes with certain data analytics challenges which, if ignored, put your efforts at risk.

As data analytics has become increasingly more influential in the business world, organizations everywhere consider it the main objective. In turbulent times to come, it is vital to get a comprehensive image of how an enterprise works and how its resources are allocated. However, in order to make the most of your data, certain issues have to be addressed, or else the data analysis process can be impeded. 

This article will focus on the biggest challenges in data analytics. Here, our customer’s experience is used to illustrate the obstacles on the way to insights, with advice from our business intelligence consultants on how to handle them.

The scary five of big data analytics

Our client is a fashion retailer looking to get their data initiatives across the goal line. However, the company’s digital transformation was put on pause as it struggled to get hold of the right data management strategy and BI tools. 

Here are the challenges highlighted by our client and the pro tips to solve them suggested by our experts.

1. Inability to define user requirements properly

The company in question has been dabbling into the potential of BI tools and data visualizations for a long time. However, our client had no hands-on experience in business intelligence, and so, couldn’t accurately define BI requirements. 

With a requirement brief on hand, it would be easier to suggest a technological solution. But our BI specialists did an amazing job analyzing the business needs and making the most accurate technical adjustments in accordance with them. Essentially transforming their demands into clearly defined objectives. 

Pro tip: If you are new to the BI ecosystem, make sure to get professional advice from an experienced technology partner. In this case, your vendor will do the heavy lifting of requirement elicitation, while you can choose one of the suggested technical solutions.

As it turns out, our client also struggled to communicate the requirements because they did not have a focus area to start with. Instead, the company was trying to encompass each aspect of business performance, from revenue to inventory turnover, and squeeze all the KPIs into one all-in dashboard. While it is certainly possible, the miscellany of metrics makes your reports less digestible.

Pro tip: Start small by identifying a few core metrics to track and visualize. By going with one functional area at a time, you increase the odds of a more successful and consistent BI adoption process and eliminate the lion’s share of data analytics challenges.

2. Carrying out system changes without considering the impact on data of other departments

In most cases, it’s not enough to map out the KPIs for analysis and tracking. For cross-functional teams, it is also important to first clarify and define what these metrics mean. As departments may do calculations in different ways, a shared understanding of KPIs and their calculation prevents the discrepancy in the value of the metrics.

On this line, our client’s another data analytics challenge was the different perspectives on the same metrics across business departments. As such, offline sales managers viewed the sales revenue as everything earned from the sale of the clothing, including the VAT charge added on top. Conversely, the accounting and finance department uses the net revenue of both online and offline sales as the core metric. 

Our team has eliminated this knowledge-sharing gap by introducing a glossary of KPIs for decision-makers. This way, each department gets a fix on the metric in use, associated data sources, and the metric formula. Decision makers also set personal ownership of each performance measure and assign a dashboard owner within a department for a given metric. 

Pro tip: Make sure you have selected the right metrics to display and are fully aware of how KPIs end up in executive and operational dashboards. Consider using separate dashboards for each department to avoid metric ambiguity and overlapping.

3. Lack of a unified corporate picture

While each business unit should get a detailed view of internal metrics, CEOs need a more high-level overview of business performance. A high-level snapshot provides a bird’s eye view of company performance and its bottlenecks. This may be done by providing an executive summary of the top KPIs across business units or by providing high-level reporting dashboards for each department in the company.

The metrics included in an executive dashboard vary by industry and company. Retailers might want to keep tabs on profit and revenue per square foot, logistics costs, profitable marketing channels, top-grossing items, gross margin return on investment, and other vitals.

As for our client, the company needed a unified monitoring report that sources and consolidates predefined values on a company’s performance. The dashboard should offer a quick health check with the possibility of drilling down into a particular metric. As for variables, our client went with three separate screens for sales analysis, profit dynamics, and customer data that demonstrate the YoY (year-over-year) growth.

Pro tip: Bring metrics by a specific department to a common view needed for executives to act on. An executive dashboard should include core, high-level metrics that can be used for a unified business strategy language.

4. Collecting meaningful data to the agreed standard

Around 95% of our clients think that it’s enough to compile each piece of business data, send this hoard to the BI tool, and call it a day. While it would be a perfect scenario, in most cases, companies are inundated with scattered data sources that lack administering. This is a textbook example of data management gone wrong.

What are the challenges of data analytics in this case? In the absence of the right data architecture, modeling and administration, your data quality does not stand a chance of producing remotely accurate and meaningful insights for decision-making. As a result, 77% of companies struggle to derive high-quality data, which leads to distrust in the organization’s data and analytics.

Pro tip: According to our big data experts, the issue of data quality cannot be solved at the analytics level. It should be eliminated by introducing a unified data management strategy within the organization. A business intelligence tool can then be used as an acid test for your data quality.

Getting back to our client, the organization had the same problem of missing data values, incomplete input, duplicates, and other snags. Moreover, the company did not have the integration architecture to share information between various subsystems automatically. 

Our team advised the organization on the best practices of collecting, storing, and transforming their data as well as took over the technical aspects of seamless data sharing between the business systems. Our data engineers identified the source of master data within each business unit (CRM, ERP, and others), selected the right data model, and calibrated the sharing process between master sources and data warehouses. 

5. Staff resistance to adopting a new system

A low adoption level of BI among your employees can also become a significant roadblock to your data analytics efforts. Currently, the ceiling on analytics adoption among its end users stands at an average of 25%, while others favor traditional spreadsheets or gut-based predictions. 

The root cause of this problem can stem from low-level skill training, the complexity of a BI tool, or the fear that automation can displace an employee. Each type of rationale should be approached individually, while the overall value of a reporting system should be clearly communicated and demonstrated to each employee. 

At first, our client struggled to get employee buy-in on having Power BI as an analytics standard. The main reason for the aversion among employees seemed to be people’s natural resistance to change.

Following the unique user needs of each business unit, our BI specialists have adjusted the interfaces to the specific department. The onboarding was followed by internal workshops for each department and facilitated by creating documents with guides on the tool.

Based on our tried-and-true experience, we know that any business transformation initiative, including big data analytics implementation, brings the most value only when process optimization, the deployment of cutting-edge technologies, and cultural shifts in people’s minds move towards each other, not further apart.

Pro tip: Low business intelligence adoption is inevitable if you neglect the role of the people involved in your processes. Build a BI interface with the end user in mind and ensure a smooth take-off by setting up single onboarding training.

Navigating your company through data analytics challenges: the easy way

Just like every house is built on solid ground, insight generation is built on a data foundation. Ironically, data itself is one of the biggest challenges in data analytics that can cripple your BI management and lead you to the wrong decision.

We helped our client to realize the full potential of data analytics and use it to their advantage. By having a unified data architecture and the right technology footing, the company can now get a real-time view of core business operations and act on the data to grow, innovate, and adapt to the fast-past retail market ahead of the competitors.