The race to unlock the potential of data is on. According to IDC, the volume of data created worldwide will increase tenfold by 2025. This means we will have even greater access to a wealth of information, enabling more powerful business insights than ever before. Most organisations already have metrics in place to understand their data. But many are merely scratching the surface and are yet to uncover true data-driven possibilities.
The idea of data creating business value is not new. Business leaders have been making decisions based on data reports for years. In today’s hyper-connected digital economy however, the ability to access data visualisation and intelligent analytics in real-time is vital to organisations looking to gain a competitive advantage. Data-driven enterprises that have access to information at their fingertips have been found to outperform their industry peers by up to 6%. For business leaders, data analytics and visualisation can make or break key conversations with potential investors, partners and shareholders.
What does the data-driven enterprise look like?
As with most enterprises, data resides across a broad ecosystem of sources. The enterprise that can leverage all data, irrespective of its source or location, will be best equipped to act on insights now and into the future.
Data-driven businesses provide a framework for users to see the whole story when it comes to data. They offer the ability for users to input and analyse all their data. Analysis is not limited to preconceived notions of how data should be structured. They recognise that it is often within combinations of seemingly disparate data that innovations occur in today’s digital era.
With most companies collecting vast amounts of data from their business operations, and the growth of publicly available data, the time is now to leverage data analytics to make better decisions and realise strategic goals. For example, e-commerce retailers, such as Lazada in South East Asia, are leveraging business intelligence to effectively compete against global online retail giants, optimise their supply chain, increase operational efficiency and better support merchant and customer transactions.
The difference between business intelligence and big data
As both buzzwords appear time and time again; it’s important to distinguish between these 2 terms and their respective roles, particularly for businesses looking to enhance their analytics capabilities and increase staff engagement with such tools.
Business intelligence (BI) is a suite or package of tools that aids various departments in the enterprise to gain insights into company performance. BI provides a microscopic analysis of data (in all forms), using understood methods to interpret it and present the information in a neat dashboard with an at-a-glance snapshot of performance, trends and goals. It can also be combined with market data to predict market opportunities.
Big data, by contrast, is not a single product but a project that requires the company to investigate the information it produces. This usually flows from high-volume, unstructured data streams, like online transactions, GPS signals and IoT information. This data, when collated and compared using large databases, can provide useful insights for the business. It can also be fed into BI tools, to produce information on predictive scenarios, like ideal costings and pricings, or products that would be financially viable to evolve.
According to Telsyte, the interaction of these data technologies is a trend Australian organisations are eager to embrace, with more than 80% of Australian CIOs planning to invest more on big data this year. At the same time, the ability to deal with this data – data literacy – has become an in-demand employee skill.
Getting better value from data analytics
To see the whole story in their data, businesses must improve their analytics capabilities. According to Gartner, organisations that deploy centrally managed, traditional BI tools are yet to engage more than 25% of their employees. To empower the remaining 75%, analytics must address business user needs where they occur. Moving towards a platform-based approach that leverages the associative self-service model can help meet this goal.
A platform powered by the associative model enables users to probe all possible associations that exist in their data, across all data sources. This means the user is not limited to predefined hierarchies or expectations of how data should be related, but can truly understand and explore patterns in the data. It empowers staff with the right tools to extract and leverage insights in real-time, without having to depend on centralised IT resources.
Beyond dedicated data scientists and business analysts, enterprises must now invest in putting the right analytics and the right data into the hands of more employees than ever before. Self-service data visualisation and BI tools are becoming increasingly user friendly, making it easier for businesses to drive data to the core of all business departments and operations. Drag-and-drop functionality, easily-interpretable visualisation features and display text are making it easier for employees to engage with business data in their day-to-day roles.
Businesses that encourage a wider range of users to experiment with and explore their data; take acceptable risks; and tolerate failure, can ready themselves for the rapidly changing world. Doing so will increase data literacy and problem-solving capabilities across the enterprise exponentially. This will drive innovation to the core of all operations and help businesses to see the whole story within their data.