For many years, companies have been accumulating large amounts of data with an intuitive feeling that it has value and would be put to good use to make more informed business decisions. As we transition into a new era where machine learning and artificial intelligence are enabling more robust analysis of a company’s data assets, it is a good time to assess your current data and whether you have the tools and processes to transform your data into actual business value.
Data as a tool, instead of an asset
To understand fully how well your company is transforming data into business value, you must first re-orient your thinking about data and its purpose within your organization. For the past 25 years, industry leaders have been describing data as a company asset – sometimes a strategic asset, other times an operational asset. The asset designation included the perception that data is something your company should strive to collect and stockpile. Unfortunately, simply possessing data doesn’t mean it is creating value for you. On the contrary, storing and maintaining data you aren’t using is actually a liability.
Data only creates value for a company when it is used to drive business decisions, establish sustainable competitive advantage and enable business agility. Data is a tool (not an asset) and value is only created when data is being consumed. This is an important mindset shift for many business and IT leaders, but essential if you want your data to make an actual difference. Instead of focusing on collecting more data (for the sake of having it), companies should be focusing on using their current data more effectively to drive greater impact.
Refining data into insights
Companies acquire data in raw form from many different sources – transactional systems, social platforms, 3rd party data feeds, data from the market, etc. Harvesting value from these data sources requires a process of refinement to convert the raw data into actionable insights. Data transformation is no different than the process of transforming raw materials into finished goods via a value stream. In this case, actionable business insights are the finished product you are seeking to provide to your data consumers.
The refinement process starts with the ingestion and aggregation of data from each of the source systems. This is often done in some sort of data warehouse. Once the data is in a common place, it must be merged and reconciled into a common data model – addressing, for example, duplication, gaps, time differences and conflicts. The unified operational data set can then be processed through a variety of different analysis functions to aggregate, summarize, correlate and create forecasts that are meaningful to data consumers. Once the data is organized and processed into informational insights, it must then be presented to data consumers in a way that is easily understood and usable for their business tasks.
Big-Data and Real-Time insights
What makes the modern era of data processing different from the past few decades is the increasing business demand for real-time insights that are informed by the holistic set of data a company has available to it. Every company now has a big-data scenario on their hands and when they combine it with the data demands of business processes that have undergone digital transformation, the need for massively scalable data processing solutions is apparent. Cloud services and distributed solution architectures, such as those Actian Avalanche leverages, provide companies with both the scale and speed they need to address big-data and real-time demands from business users.
Transforming data from a set of assets that you simply possess into a set of actionable insights that are actively being used across your business to make decisions is the key to developing sustainable competitive advantage in the modern business climate. You’ve been collecting data for many years, isn’t it time you use it to make an actual difference for your company? Actian can help. To learn more, visit www.actian.com/avalanche.