Slow data kills business
More and more businesses today are looking to extract insight from the data they have access to in as near real-time as possible. They are looking to rapidly gain the insight and intelligence they need to drive optimum customer experiences; faster time to value and a competitive edge.
These are elusive goals for many businesses though. Companies need to be able to blend analytical queries and transactional data. Otherwise, they are likely to be basing decisions on data that is anywhere from ten minutes to two hours or even days old, making it all but impossible to capitalise on many real-time and near real-time Business opportunities.
It is hardly surprising, then, that, according to Choosing a DBMS to Address the Challenges of the Third Platform, May 2017, an IDC InfoBrief sponsored by InterSystems, 76 per cent of respondents reported that the inability to analyse current data inhibits their ability to take advantage of Business opportunities.
In assessing how a combination of transactional and analytic data processing can help here, it makes sense to first consider their respective roles within the enterprise. One of their distinguishing features today is simply that they are separate. Indeed, transactions and analytics typically form two distinct data processing arms within the enterprise.
Transactions often involve the processing of records data in relation to regular operations conducted across the entire business and are designed for write, not query, speed. analytics process data from multiple transactional databases and are designed for query speed to provide organisations with insights based on specific questions.
Data often needs to move from transactional systems to , increasing complexity and latency that slows the business down and can lead to missed opportunities. Transactional data processing is often limited in its ability to quickly perform analytic queries, while analytics data processing is often too slow to deliver valuable real-time insights. A transactional approach drives business operations. Analytics make the data actionable and bring out its value, empowering organisations to identify connections across multiple transactional databases.
According to the IDC study, 86.5 per cent of organisations use ETL to move at least 25 per cent of all enterprise data between transactional and analytical systems. And nearly two-thirds (63.9 per cent) of data moved via ETL is at least five days old by the time it reaches an analytics database. This is a critical obstacle for most organisations that want to deliver the right customer experience at the right moment.
Businesses do, however, also face other hurdles on top of this. Typically, they will need to support more data types (structured, , etc.), larger data sets and an accelerated path from analysis to action introduced by mobile users, IoT/sensor data, and fickle / constantly emerging trends.
This situation is not helped either by the disparate range of data management tools they typically use today. Companies often utilise several different database systems, for example, which means the data is saved and stored in a wide range of different places and formats. Each database is unique to the types of data and types of workload it specifically manages.