How can you move your big data from reactive to predictive analytics?
Big data analytics is one of the biggest trends in the IT sector at the moment, with businesses across all verticals looking to see how the latest developments in this field can help improve their operations and provide better insight into their customers and activities
In the past, business intelligence was a very reactive process, involving the analysis of historical data in order to forecast future demands and trends. But this can be a slow, complex activity, and there are no guarantees concerning its accuracy – after all, what worked in the past may not work in the future, and trends and customer preferences evolve over time.
Today’s business environment is a fast-paced, constantly changing landscape where these traditional solutions will not be good enough. Growing data volumes pose a serious challenge to many companies, and existing analytics and intelligence tools will struggle under the burden.
Any delays in delivering results will be hugely costly, with businesses losing out to more agile, forward-thinking competitors that are able to more accurately predict forthcoming trends.
A new standard for analytics
Predictive analytics are transforming the way companies in many industries operate. For example, in the healthcare sector, this technology can be used to spot potential public health trends before they become apparent, allowing organisations to prepare for and prevent any serious issues.
Elsewhere, the telecommunications industry can use predictive analytics tools to identify red flags that may be key indicators of unhappy customers, enabling them to take steps to improve their relationship even before the individual has registered a complaint.
Meanwhile, the manufacturing and transport sectors can combine predictive analytics with IoT technology to alert them when equipment is in need of maintenance.
With almost every vertical able to apply big data analytics to its strategic decision making, it’s no wonder the sector is set to see a huge uptake in interest, with International Data Corporation estimating that predictive analytics applications will grow 65 per cent faster than business intelligence tools without this functionality in 2015.
The tools needed for success
In order to take full advantage of these solutions, it’s vital that businesses have the right technology in place. With big data analytics now being asked to gather huge amounts of information from a wide variety of sources, and process this at very high speed in order to deliver results, outdated business intelligence and analytics tools will not be able to cope.
Therefore, companies will need to look at solutions that can deliver lightning-fast results and handle a wide range of both structured and unstructured data, which will be key to the success of any predictive analytics deployment.
One technology that may be highly useful is in-memory processing. This can help speed up data warehousing performance by up to 100 times compared with traditional solutions, something that’s vital for real-time and predictive results.
Strong scalability will also be a must, as data volumes are not likely to stop growing any time soon. And as the technology matures and businesses become more familiar with its capabilities, it is to be expected that new use cases and scenarios will continue to be discovered.
That’s why powerful options such as Microsoft SQL Server are invaluable to forward-thinking businesses looking to boost their analytics capabilities. With fast, reliable performance, high scalability, the ability to connect on-premises and cloud solutions and integration with familiar analysis tools such as Excel, it can give businesses all the features they need to successfully make the move from reactive to predictive analytics.
Find out how SQL Sever can help transform your operations
