Real-time analytics are essential for many companies, after all, fast and accurate insights are essential in the data-driven age. This is how real-time data analysis works. […]
Businesses are flooded with data of all kinds at unprecedented speeds. This makes direct access to insights in real time all the more important for decision-makers. Organizations can meet this challenge with real-time data analytics.
Like any other powerful IT tool, realtime analytics requires a high level of understanding and expertise. This is the only way they can be used to meet critical business requirements, such as workflow optimization or customer analysis. The following seven tips should be followed by your company if it wants to take the first steps in real-time analytics and avoid various pitfalls.
Business and IT decision-makers often tend to be speed-loving. This means that all insights should simply be available in real time. This is a waste of money in some cases and counterproductive in others.
“For example, it doesn’t make much sense to put the revenue report in a real-time analytics environment, especially when instructions are often canceled, moved, or adjusted,” said Theresa Kushner, senior director of data intelligence and automation at NTT Data Services. She points out: “How would a sales manager react if she reached her goal one minute and fell to 88 percent in real time the next?“.
By developing an understanding of which analytics can truly benefit from real-time, IT can generate significant value for the business. “As with any analytics initiative, you need to have a strategy and know what decisions to make based on real-time analytics,” Kushner concludes.
If it is the expectation within a company to get data insights in real time, there can be disappointment if the IT infrastructure does not play along in terms of performance.
For long-term success with realtime analytics, the underlying architecture must also support real-time data collection and processing. Dan Simion, Vice President AI at Capgemini North America, is also aware of this: “The models must also be designed to support the processing of real-time data. The data sources must be real-time, not near-real-time or in daily generated batches.”
Before embarking on a real-time analytics initiative, the project leader should ask end users which dashboards they need. “With this information, the IT leader can have his team review the data collection requirements and ensure that the real-time analytics solution can provide the information accordingly,” said Rich Temple, vice president and CIO at the Deborah Heart and Lung Center.
This approach allows IT to put the end user at the center of the analytics discussion. “Instead of ‘pushing’ a solution that might inhibit workflows, you should take the trouble to identify the needs, ” says Temple.
The value of real-time data increases exponentially when it is merged with historical data, notes James Corcoran, senior vice president of engineering at analytics provider KX.
“Take, for example, the temperature data transmitted by a sensor embedded in a machine. Understanding such data in real time is useful to check whether the machine is operating efficiently or whether a temperature threshold has been reached,“ explains Corcoran. If historical data were also mapped over days or weeks, decision-makers could gain a more comprehensive understanding of how a particular machine works.
Corcoran describes this methodology as” continuous intelligence”: the ability to make smarter decisions based on the insights gained from analyzing data – whether in real time, historically, or both – in the shortest possible time frame.
IT decision-makers should include not only internal but also contextual data regarding competition, markets, customer segments and census data points in the analysis. Thus, realtime analytics are able to provide a comprehensive set of facts and trends, recommends Sumit Anand, CIO at the US retailer At Home: “There should also be direct input into the business technology roadmap and the long-term financial planning of the company”.
Long-term access to high-quality, internal and contextual real-time data allows companies to change their decision-making process, says Anand. “The approach is effective because it focuses on changing an organization’s culture.“
“Relevant Information” is information that causes a recipient to change his or her thinking about a particular matter, judgment, or course of action.
“According to statistics, hardly anyone still checks the oil level of his vehicle before a trip. But if an alarm sounds or lights up, the driver will take the next exit to look for a solution,“ explains Kenneth McGee, research associate at Info-Tech Research Group.
Executives and managers would be flooded with information – far too many to ever fully absorb. “However, a small amount of relevant information analyzed in real time is enough to ensure success,” McGee notes.
Your analytics team should be a true partner, not just a contractor: “If the team is seen as a business enabler rather than a cost center, the company is willing to invest more in human and technical resources to support real-time analytics,” advises Kathy Rudy, partner and chief data and analytics officer at Information Services Group.
The best way to ensure long-term success is to equip the analytics team with business knowledge so they can provide relevant information, Rudy said.
Over time, a well-supported analytics team will be able to deliver increasingly relevant data that will enable decision makers to take swift and informed action. “This includes bringing in market data through API connections and data scraping to support internally generated analytics,” says the ISG analyst. “Being available as a partner with real-time analytics and making the right recommendations to the leadership team can make you a rock star.“
* John Edwards is a freelance writer.