Data analytics – expensive, complex but worth it
The importance today of being a data-driven business today is regularly hammered home in biz-tech media circles.
And behind that repetition is a lot of substance; as increasingly digitized organizations, we’re amassing more data than ever before – tapping into the insights it holds can inform and transform approaches inside and out of the business, be it learning about customer preferences or how to streamline our recruitment process, that ultimately can make us market leaders.
According to a new report by SQream, 59% of companies surveyed expect to see an increase of over 50% in their data volume in 2021. 60% of the surveyed companies already have over 500 TB of storage, meaning the expected increases will be enormous amounts of data.
The study found that 99% of management teams understand how critical data analytics is to make informed business decisions today. But the constant pressure to maximize the use of this ever-expanding wealth of data to derive ‘real-time insights’ and ‘data culture’ – and to avoid ‘dark data’ at all costs – likely induces a pang of anxiety among even hardened business leaders who find the task of being data-driven resource-intensive and mountainous.
While management teams want to prioritize data analytics, they generally don’t have the budget necessary to cover the costs. Only 13% of the companies surveyed were in a good financial position when it comes to supporting data analytics activities. Over half (55%) will not fare as well, as their budgets will cover less than 75% of their actual data analytics needs in 2021.
According to SQream, it remains to be seen if shifting priorities to focus on analyzing the treasure trove of data that companies are gathering will result in a matching shifting of budgets.
The expense comes with the need to invest in and effectively implement solutions and tools to analyze data, such as Hadoop, Spark, HANA, and Tableau. But even with sufficient technology in place, organizations need to recruit and commit talent to utilize these tools effectively and contribute to cheerleading and embedding a culture of data analytics within the business.
KPMG suggests that organizations with a CDO are twice as likely to have a clear digital strategy. The ongoing education of the organization’s workforce is a fundamental part of such a strategy.
On top of the expense of data analytics tech and talent, 82% of companies shared a variety of challenges related to how long it takes to prep data, ingest data, run queries, and create analytic reports. Only 18% of surveyed companies claim to have no challenges when it comes to running data analytics.
Indeed, IBM found that even if you do invest in professionals such as data scientists, up to 80% of their time can be spent data cleaning, or making it ‘usable’ for analytics and other applications.
When it comes to becoming a ‘data-driven’ company, then, it seems there aren’t half measures. Organizations that want to leverage as much of the potential of their data as possible must make the initiative a priority central to their business strategy.
That said, it won’t happen overnight. An interview with Asian pharmacy giant Zuellig Pharma on Tech Wire Asia revealed how a well-thought, step-wise process can be the most effective. After investing in the necessary infrastructure, the firm focused on training staff on the benefits of using data and analytics, it educated clients and customers about the value of this new information and built a robust but agile data governance framework.
“As traction among staff, clients and customers grew, so too did our data culture and the number of ROI accretive use cases,” said Zuellig Pharma’s VP of data & analytics, Tristan Tan.
Only after putting the foundations in place could the company explore more advanced areas: “[…] we have now started leveraging blockchain in our handling of information with our partners across the supply chain and have embarked on organization-wide automation agenda using data-science and robotics process automation to drive efficiencies in our operations,” Tan said.
“These efforts would not have been successful without the foundations we built in the earlier phases of our journey.”
Now the company has a “critical mass” of people who understand the importance of ensuring data is informing the work being done across the organization. Data culture is not an intangible concept but is embedded practically into business processes.
“Ensuring that a piece of information, a data report, a dashboard or analysis, is a regular part of normal business process has been key to our data transformation,” he said.
Even for some of the most advanced, ‘data-native’ companies, a piecemeal process of individual education and daily discipline has been crucial. UK fintech Revolut is one of those; it maintains around 800 dashboards and runs around 100,000 SQL queries on a daily basis across the organization, and can optimally analyze large datasets spanning several sources to assist in fraud detection, improving customer satisfaction and financial reporting. Queries that used to take hours are now completed in seconds, enabling self-serve data analytics for all employees across all business functions.
But behind this success with data analytics is an objective to ensure every employee at Revolut has access to the data they need for their work, every day, in a simple and efficient manner. The data science teams uses the central database as a single point of truth, from which it can download real-time extracts and insights at any time.
This is just one example of a company that has built a culture of data literacy from the first instance, but it showcases the potential of making data analysis and intelligence central to the entire business and making it a day-to-day discipline.
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