A Laundry List for Cleaning Messy Data and Making It Business Ready

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In this special guest feature, Mark Palmer, TIBCO SVP & GM of Analytics, Data Science & Data Virtualization, believes that companies who learn to leverage their data will beat out their competition. Mark holds nearly three decades of experience working in the financial technology industry, creating innovative technology, taking products to market, and building companies. He has extensive expertise in algorithmic trading applications and automated trading architecture development and is actively using his skills to help TIBCO create a next-generation “Digital Business” software infrastructure and analytics platform.

Data is critical to analyze customer behavior and predict risks, but many companies are unable to comb through their mounds of data to derive useful insights. Businesses understand that data is crucial to their overall strategy. In order to make the strategy a reality, they need to prepare and utilize their data – a skill that is not always immediately available.

Research shows that leaders believe data is extremely or very valuable to their organization in terms of overall success (81%) and innovation (75%). A few steps to maximizing this success and innovation include: mastering the basics of data preparation, choosing reliable – and useful – infrastructure, and utilizing automation.

Back to basics

There are a number of fundamental practices that companies must master before tackling visualization or analytics, which lead to informed business decisions. First, start by making sure you have all the correct data. To make sure your data is on the right track, be sure you’re asking the right questions of your data and collecting data in a central location. Next, ensure your assets are clean and trustworthy through data preparation and the removal of any erroneous information. These steps are key to kick starting the data-informed journey.

Tools of the trade

In order to make the most of your data, you have to be able to access it. Once you understand the measures you need to take to clean and prep your data, you need to get the right tools to make it happen.

Start out with a tool that allows visibility into your data sets. The tools you use should be able to break down silos between your data, integrate all the information you have and access the full power of your data. Only then can you consider how to derive insights from it. In fact, lack of proper infrastructure and tool challenges not only hinder progress but actually slow down access to data and limit your ability to offload mundane tasks that take time and distract from focus/productivity.

Once the questions of infrastructure and accessibility are answered, companies can consider moving onto the next step of analyzing and visualizing this information. Global data visualization market size stood at $8.85 billion in 2019 and is projected to reach $19.20 billion by 2027. These numbers show that visualization is growing at a rapid rate, and it will be necessary to stay relevant in the years to come.

Make the most of automation

One of the smartest ways you can improve business operations is with digital process automation, which allows you to manage and optimize your enterprise resources through automated tasks and processes. A good process management platform enables end-to-end execution of long-running processes while providing real-time visibility and insights. As with infrastructure, improper, or non-existent, automation techniques can slow down the data strategy and the timeline of data scientists working on these projects. In the end, automation and intelligent services help humans do the right thing at the right time with the right knowledge. This knowledge drives the best and most strategic business choices.

Companies who learn to leverage their data will beat out their competition. Making data-driven decisions for business strategy is essential in today’s tech-centric environment, and anyone who is not taking advantage of the information they’ve gathered will fall behind. In order to keep up, companies must clean and make sense of their messy data by returning to the basics of their data management, implementing the proper tools for the job and utilizing automation and analytics.

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