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Using data to build the muscle memory for successful business agility

(Image credit: Image Credit: Shutterstock/Sergey Nivens)

Over the past few months, the importance of agility has been an inescapable topic in business commentary. While understandable given the great shifts required in a very short space of time at the pandemic’s beginning, you might assume it’s just during periods of great flux that companies should be focused upon it.

Agility has always been critical to business success. No company is immune to becoming redundant. Agility is crucial to manage through market changes, technological innovation or shifts in working practices. Most of the world’s most successful businesses are characterized by their agility: Netflix’s humble beginnings sending out physical DVDs are remembered by few of its customers today, given its successful and redefining pivot to streaming in the face of changing consumer consumption (unlike competitors, like Blockbuster).

Whether you’re responding to rapid shifts in working environments or forecasting long-term customer trends, agility critically underpins a business’s ability to shift and adapt to protect and improve its performance and productivity.

However, adopting an agile approach doesn’t come without its risks. And these are felt more acutely during highly volatile climates, like the unprecedented one that in which we’re currently operating. This put a finer point on how decisions are being made, since 71 percent of business leaders that continue to frequently defer to their gut for decision making which puts changing course at risk and takes their company down completely the wrong path.

To make agile decisions that will improve productivity and performance with confidence, it is critical to truly understand the situation in which you’re operating. This can only be achieved through access to and the analysis of accurate, clean, trusted and timely data.

Poor data makes for poor decisions

New research from Qlik in partnership with IDC has exposed the significant and pervasive issues that global organizations face in creating a strong data pipeline that identifies and readies raw data for analysis. Just over half (57 percent) of businesses believe that they’ve found and captured most (70 percent+) of the valuable data sets from across their organization, but they then consistently report challenges in capturing and processing it.

The rewards are plentiful when you get it right: successful investments in data management and analytics have been shown to improve both productivity and performance. In fact, three-quarters of organizations reported that their operational efficiency, revenue and profit all improved by an average of 17 percent.

So, what are the key considerations for organizations looking to improve their ability to use analytics for a data-informed approach to agility?

1)            Do you trust the data?

With the drive to become data-driven businesses, we don’t often question data as much as we should and, in turn, frequently make decisions on inaccurate insights.

Before using data analysis to inform your decision, it’s important to ask yourselves whether the source data falls into any of the common traps that impact its trustworthiness. Is it complete? Is it correct? Is it secure? In our survey these were all cited by business leaders as some of the greatest challenges they faced in capturing and processing raw data (40 percent, 42 percent and 38 percent).

It is also important to question whether this is even the best data for you to be making this decision from. Nearly all (94 percent) of global organizations struggle to identify potentially valuable data sources. Rightly this is the area of the data pipeline that one quarter of companies (25 percent) are making their greatest investments in over the next 12 months. Understanding what data your organization holds is the only way to ensure you’re using the best information at your disposal to make decisions.

2)            Are you moving fast enough?

It is not enough to have data – is it up to date and relevant to the moment in which you’re making the decision? Nearly one-third of business leaders report that not having data available in a timely fashion is one of the most common reasons that analytics projects have failed.

While historically getting access to analytics-ready data from some sources (including as transactional data from ERP or CRM systems) might take as long as six to nine months, due to the cumbersome extract, transform, load (ETL) process, that need no longer be the case. With Change Data Capture (CDC), organizations can stream real-time information, regardless of source or schema, into data warehouses or cloud-based platforms where it can be automatically prepared and provisioned for analysis. This process reduces the time to turn data from raw to ready-for-analysis from months to minutes.

3)            Are your team trained to use it?

Ensuring your team has the skills to use analytics to inform decision-making is integral to making agile decisions across the business. In fact, the need to improve the training of knowledge workers was voted the second most critical area that would increase the success of data analytics projects by business leaders. This is perhaps unsurprising given just 21 percent of the global workforce are fully confident in their data literacy skills and, when overwhelmed by data, employees report finding alternative methods to either completing tasks without using data (36 percent) or completing the task entirely (14 percent). Educating and empowering your team to understand and question data will be critical to identifying the opportunity to increase operational efficiencies and productivity, as well as identify new trends, that will enable your business to become truly agile.

Are you relying on luck or insights?

I doubt that there has been a single business across the world that hasn’t had to make agile decisions in response to the recent crisis. Yet of those companies, how many can confidently say that they were based on trusted insights? It’s important that companies learn from these past months of rapid agility to understand where the leaks are in their data pipeline which are preventing those decisions being underpinned by data.

James Fisher, Chief Product Officer, Qlik