To say that today’s business environment is in uncharted territory is understating reality. Although there are signs of an economic revival, the effects of the COVID-19 pandemic will reverberate for some time.
Why even mention such a challenging time in an article about data preparation? There has been a profound paradigm shift in how data is used for analytic purposes. Data science models that relied on vast amounts of historical data are no longer valid, there has been a 180-degree change in consumer behavior across the globe. With such a drastic change in consumer behavior and preferences, a lot of data that was used to make decisions from is no longer relevant.
Businesses have responded by setting existing analytic models aside and have added more people in the trenches to make decisions about how to support rapidly changing and unpredictable customer demands. Since machine learning (ML) models are no longer accurate, people are manually sifting through vast amounts of data, creating new reports that are fed into visualization tools. Undoubtedly, they are overwhelmed, and this pattern is not sustainable.
Prior to the pandemic, there were challenges in using data. Oxford Economics noted that 45% of companies were not using data to make decisions. Of the data that was being used by the remaining 55% of organizations, 97% of it was not of acceptable quality. It’s not surprising to hear then, that on average 77% of companies believe they have experienced revenue loss of 12% or higher because of bad data. In the current environment, the impact of inaccurate data is likely to amplified.
Simply stated, data analytics today requires more accuracy than ever before. For many industries impacted by the pandemic, understanding changes in consumer behavior is a top priority. For example, how should a marketing team promote an offering when consumers are now largely ignoring traditional marketing efforts? Not understanding behavioral changes can lead to the wrong conclusions on where to invest new marketing dollars, or adjusting supply chains to meet changes in demand. Convenient sample measuring can no longer be accepted when ascertaining the impact of a new marketing initiative; an inaccurate conversion rate understanding will have significant and likely detrimental impact on further budget allocations and return on investment. To counter this, more representative measuring is required, which leads to a deeper analysis of data and more precise customer segmentation. This begins by rapidly on-boarding, appending, and joining multiple sources of new information, converting this into data that will more accurately convey changes in consumer behavior.
For 30 years, Altair Monarch™ has helped customers create accurate, trusted datasets in a matter of minutes. The most popular business intelligence (BI) and analytics tool on the market today is the spreadsheet, and 88% of those are inaccurate if they have more than one expression in them. Monarch converts non-tabular layouts and hierarchies generated from business report systems, or people who manually create reports and forms in Excel, into accurate and trusted data models and datasets.
Even more powerful is Monarch’s ability to cleanse and join data from formats such as Excel, PDF, and text, and directly access tabular databases and business systems across the organization like Salesforce, Netsuite, and Google Analytics. Monarch’s unique reach into the widest variety of company information assets provide a larger picture for analytics.
Eventually the pandemic will subside, and data analytics will settle into a new normal. Until that time, agility, ease of use, self-sufficiency and efficiency is required to help reduce the burden on the workers in the ‘data trenches’ who are working with large amounts of data that is reflective of the paradigm shift the pandemic has brought. Monarch will assist data analytic teams quickly process this vast amount of disparate data. With accuracy and trust comes the comfort that the data being used is the right data to make decisions with.
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