With various types of data pouring into enterprises at an unprecedented speed, it is becoming a business need to enable decision makers to easily obtain immediate insights. Real time analysis enables organizations to meet this challenge by applying logic and mathematics to raw data, transforming numbers into actionable knowledge, and opening the door to fast and accurate decision-making.

Like any powerful it tool, real-time analysis requires a high degree of understanding and proficiency to meet key real-world business needs, such as enhancing workflow, promoting marketing and sales planning, and understanding various types of customer behavior.

Here are seven ways for organizations to start using real-time analysis to help you avoid common mistakes and promote technology to its maximum potential.

1. Limit real-time analysis to relevant use cases

Business and it leaders are so obsessed with speed that they insist that every data insight is delivered in real time. However, in some cases, providing real-time analysis is not only a waste of money, but also may be counterproductive.

“For example, it may not be appropriate to put your financial revenue report in a real-time analysis environment, especially when orders are often cancelled, moved or manipulated,” said Theresa Kushner, senior director of data intelligence and automation of NTT data services, an it and business service provider“ If the sales manager reached the target one minute and dropped to 88% the next, how would she react? “

By understanding which analytics can really benefit from real-time support, it can ensure that real-time analytics programs can really bring significant value to the enterprise“ Like any analysis, you need to have a strategy to determine what results the analysis will produce and what decisions to make from the analysis, “Kushner concluded.

2. Build a strong and reliable infrastructure

When an enterprise expects accurate real-time insight, but cannot achieve these goals because its infrastructure does not meet the planned performance level, it will be disappointed.

Dan SIMION, vice president of artificial intelligence and analysis of Capgemini North America, a business consulting company, said that in order to ensure the long-term success of real-time analysis, the underlying architecture also needs to support real-time data operation, ingestion and processing“ We also need to build models to support the processing of real-time data, and the data source must be really real-time, not near real-time or batch data generated every day, “he added.

3. Deploy appropriate dashboards

Before advancing any real-time analysis plan, the project leader should also contact the end users to determine the type of dashboard they need“ With this information, the IT leader can let his or her team review the data capture requirements from the source data and ensure that the real-time analysis solution can provide the information they need in the format required by end users, “said rich temple, vice president and chief information officer of Deborah cardiopulmonary center.

Temple explained that this approach allows it to put end users at the center of analysis and discussion“ Before trying to purchase and deploy the analysis system, don’t force an analysis solution that may disrupt the user’s workflow, but first determine the requirements and provide deep insights into the possible situation, so as to produce analysis for the needs of individual users, “he said.

4. Combine real-time data with historical data

James Corcoran, senior vice president of engineering of KX, a data analysis software developer and provider, pointed out that when real-time data is combined with historical data, its value will increase exponentially, enabling end users to integrate and compare insights “in the moment”.

For example, consider the temperature data transmitted by sensors embedded in the machine“ Understanding these data in real time is very useful to check whether the machine is running efficiently or whether the temperature threshold is reached, “Corcoran explained. When historical data can be mapped in a few days or weeks, decision makers can better understand the operation of a specific machine“ You can also build prediction models based on the performance data of other machines, “he added.

Corcoran describes this approach as “continuous intelligence” – the ability to make smarter decisions in the shortest possible time based on the insights gained from data analysis, whether real-time, historical or both.

5. Combine internal and contextual data

It leaders need to ensure that their analytical practices can absorb not only internal data, but also background data related to competition, market, customer segmentation and demographic survey data points to provide a comprehensive set of facts and trends, suggested sumit Anand, CIO of home decoration chain at home“ There should also be a long-term financial and technical investment roadmap for the organization, “he said.

In weeks or months, access to real-time internal and contextual data, as well as rich and meaningful insights, can change an organization’s decision-making process, Anand said“ This approach is effective because it focuses on changing the corporate culture through more data-driven, fact-based and macro understanding of the business prospects of the enterprise. “

6. Focus on delivering substantive information

“Substantive information” is a concept borrowed from law and accounting. It refers to information that can enable the recipient to change their thoughts on a specific problem or change their judgment or action process“ They will respond to the substantive information they receive, “said Kenneth McGee, a researcher at Info Tech Research Group, a research and consulting firm.

For example, during the journey, the vehicle’s fuel gauge will only be checked once (if any). That’s static information“ However, if an alarm lights or rings, the driver will immediately consider seeking a solution at the next exit, “McGee explained.

Executives and managers are flooded with information – too much information to be fully absorbed“ However, to ensure success, only a very small amount of substantive information needs to be analyzed in real time. ” McGee pointed out.

7. Establish an analysis team that understands key business requirements

The analysis team should be a real partner, not just an order recipient“ When the analysis team is regarded as a business driver rather than a cost center, enterprises will be willing to invest more in human resources and technology to support the analysis function, “suggested Kathy Rudy, partner and chief data and analysis officer of information services group, a technology research and consulting company.

The best way to ensure long-term success is to provide the analysis team with business knowledge to provide relevant intelligence, Rudy said“ The more they know about the business, the better they are at providing important analysis. “

Over time, a well supported analysis team will be able to provide more and more relevant data so that decision makers can take more rapid and wise actions“ This includes using API connections and data capture to introduce market data to support internally generated analysis, “Rudy said“ You need to be a partner through real-time analysis and tell your senior management team what they don’t know or even want to ask, which will make you a rock star. “

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