TMCnet Feature
May 27, 2020

7 Strategies for Improving Real-Time Data Analytics



How to Improve Your Access to Real-Time Data

Real-time data is becoming more important in a number of industries and applications, thanks to its high accessibility and the inherent advantages of getting eyes on data in a constant flow. However, if you want your data analytics strategy to succeed, you’ll need to work proactively to improve it.



How to Improve Real-Time Data Analytics

These are some of the most important strategies you can employ to improve your real-time data analytics strategy:

  1. Use better APIs. First, you’ll need the help of better APIs for your given application. APIs work by feeding information from one data source to another application; for example, stock APIs work by sending real-time information about the stock market, including the prices of individual assets, to another application like a dashboard or algorithmic trading platform. This is your primary source of real-time data, so you’ll want to make sure it’s reliable. For starters, you’ll want to choose an API that’s appropriate for your chosen subject or field; you’ll also want to run regular checks to make sure data is being transmitted both consistently and truly in real-time. If there’s an error or flaw in transmission, it could disrupt your entire analytics approach. Additionally, you’ll want to ensure that this API connection is secure; APIs often serve as attack vectors for cybercriminals and represent a point of vulnerability.
  2. Institute more checks and balances. Real-time data can be mishandled or distorted in a number of different ways. Your source may have bad data, your API may be misfunctioning, and more commonly, your team members may misreport or mishandle your data when they gain access to it. You can minimize the risk of an error and improve your organization’s overall approach to analytics by instituting more checks and balances. Ensure multiple verification points, and double check your work before moving forward with a decision.
  3. Incorporate data analytics at multiple levels. Your organization may have full-time data analysts and data scientists on staff, but data analytic capabilities are important for every member of your team. Exposing team members on different levels and from different backgrounds to your data analytics processes can help ensure the entire organization functions more smoothly—with more accurate analyses of your data. Top-level data scientists can focus on big-picture questions and complex number crunching, while other members of your team can be taught to make quick, low-level decisions based on the data they have available to them.
  4. Rely on visuals for fast analysis. Data visuals have revolutionized the real-time data analytics world. While we’ve always used charts, graphs, and other simple visuals to make data easier to understand and present, the fact that you can quickly and easily manipulate these visuals with new variables in the modern world lends itself to much quicker analyses. Armed with the understanding of how data visuals work, your employees can quickly form impressions about real-time events, and witness how they’re unfolding over time. Just be careful; while data visuals are helpful in forming impressions and making predictions, they can’t tell you everything.
  5. Don’t neglect deep dives. Glances at data visuals will help you get started on the road to deeper analysis, but it’s still important to take deeper dives. Real-time data is best for immediate observations, ongoing monitoring, and fast decision making. But if you want a more thorough understanding of a given event, or your organization’s functionality overall, you’ll need to conduct deeper studies. You’ll also want to look closer at outliers, which have a tendency to get ignored by data visuals.
  6. Tie data to decision making. Data analytics is designed to help you make better decisions; it doesn’t have much value unless you act on it. Accordingly, you need a system to die your real-time data analytics to decision making. Which metrics and variables are most important to consider? Which trigger points should motivate your employees or leaders to take action? The better documented these approaches are, the more consistently your team will be able to act.
  7. Reflect and improve. Finally, take the time to evaluate your data analytics methods and processes. Are there errors or misguided decisions that could have been prevented or mitigated? Are there new tools or approaches you could take to streamline the process? Are there any additions or changes you could make to reduce the amount of time you’re spending?

Adapting to New Technological Trends

It’s also important to stay abreast of new developments in the data analytics field, which is evolving at an incredible rate. New tools, new dashboards, and new methods of data analysis are vital if you want your organization to succeed. Keep auditing your existing processes and staying flexible with regard to new ways to improve.



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