Why technology alone won’t solve the analytical challenges with your data

Why technology alone won't solve the analytical challenges problems with your data

The world of data and analytics is in constant evolution and organisations have to keep up to stay ahead. However, too often, business leaders jump straight to a technological solution to fix their analytical challenges. 

While technology is necessary and helpful, it is not an all-in-one solution. Without the right strategic thinking and processes, you could end up with a failed tech implementation and wasted investment.

Instead, the current business landscape requires organisations to nurture analytics talent and centre data processes around their people in order to keep pace.

In this blog, we explore two common analytical challenges and why technology alone won’t solve them.

Challenge #1 – Collecting the right data

The greater the volume of data, the greater complexity of your overall data environment. Many organisations gather data from multiple sources, especially for data enrichment activities. However, this is only useful if you have a good strategy in place for consolidating all that information so sense can actually be made from it. In other words, if it’s not the right data in the right place at the right time, then it is essentially meaningless.

This creates a number of pain points for your people. Staff may spend hours sifting through information to find what they need, affecting business productivity and efficiency – not to mention –  causing frustration. Additionally, the wrong data in the wrong place will affect the relevance and accuracy of your reporting, leading to poor or blind decision-making.

Unfortunately, technology by itself isn’t able to judge what information is the right information in complex data environments. That’s where human expertise comes in.

Solution: Clear business requirements and strategic context

Companies with complex data environments require robust guidelines for retrieving, transforming and classifying the information used in their data analytics. While the right software can assist in performing these actions, it still requires a human to make strategic decisions about which information is relevant and useful, and ensure that it is handled in a way that aligns with business policies and goals.

For example, the goal of data analysis is to tell a story that is relevant to your business’ strategic context. This requires defining the critical questions that your data analysis must answer, determining what data needs to be collected to achieve that purpose and choosing the most helpful way to present it. This is something only people can do because they understand the company’s goals and strategic direction.

Sure, there’s no question that machine learning and data visualisation technology is useful in helping optimise insight generation. But it can’t decide which metric is the most important measure of success in your particular business context. Ultimately, the effectiveness of your data analytics is dependent on technology that is governed and informed by strategic, human capability.

Challenge #2: Poor quality data

Business leaders are increasingly aware of the importance of data quality and its critical role in data analysis. That’s because poor data quality can lead to unreliable insights, false reporting and bad decisions. At best, your data is misleading. At worst, it’s not usable.

Some causes of poor-quality data can be mitigated with technology, such as automation reducing the risk of mistakes from manual data entry. Similarly, technology can be helpful for data cleansing and validation to improve the quality of your database. However, it cannot guarantee data quality by itself.

Solution: Data quality controls data governance

High-quality data relies on having appropriate data quality controls in place. These controls should form part of a wider data governance framework that defines how data is handled in your organisation. This is where technology falls short as people have to be the ones to devise the business-specific data quality metrics that meet the criteria of your data governance framework. They also have to be the ones to uphold these metrics, assess whether they are working, learn from past mistakes and make continuous improvements to your data governance policy. While technology might be able to identify anomalies and outliers, it takes a human brain to look at a whole dataset and realise that something isn’t quite right with the overall quality of data.

There’s no substitute for human expertise

While technology plays an essential role in driving data analytics as a differentiator, it cannot replace human expertise entirely. The most successful data projects happen when people with domain knowledge and skill are at the wheel of your tech stack. Subject matter experts are essential for ensuring that you have the right data to meet your needs and that it meets your business’ quality controls. Applying technology without doing due diligence and appropriate planning is likely to result in a solution that doesn’t integrate with business use cases nor meet the strategic needs of your organisation – leading to frustrations, missed opportunities and poor decision-making.

Want to learn more about how to solve your data and analytical challenges with the right strategic solution? Download our free eBook to learn how your organisation can improve data quality and data governance to gain more value from your business intelligence.

Optivia’s data quality services empower reliable, informed decision-making. We help you strengthen your business with robust data governance principles designed to improve data accuracy and ensure you adhere to data privacy regulations. If you need a data quality framework that aligns with your strategic goals and helps you make rapid, meaningful decisions, contact us today.