Four Steps To Transform Your Company’s Raw Data Into Useful Insights

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Negosentro| Four Steps To Transform Your Company’s Raw Data Into Useful Insights |With ever-improving digital storage technology and ever-expanding options for collecting new and different forms of data, it’s easy for businesses to create undifferentiated data lakes flooded with raw information from user behavior logs and partially completed forms to recorded sensor input. Plentiful storage on redundant file servers ensures that data never has to be deleted—so it keeps piling up.

Most companies grasp the value of collecting and retaining information, so there’s plenty of material, but when nothing is sorted, deleted, or examined, converting data to insights can become a near-insurmountable task. If this is a frequent problem for your company’s metrics and insight team, take the following four-part approach to extract the information you need from the data you have.

Step One: Process and Examine Data

The drive to collect more and more data has led to the normalization of multiple input streams to just one or two databases. Unfortunately, it’s difficult to ensure that each source is sanitized before adding results to a database; various inconsistencies combine with user error to produce huge amounts of data filled with empty fields, syntax errors, duplications, and more. As a result, when you’re tasked with analyzing a dataset, the first task is proper data cleaning.

Managerial staff with limited technical expertise may balk at spending time on the cleaning and preparation part of the analysis process; it requires vastly more time and attention than any other step, and because new data is arriving all the time, it has to be repeated frequently. Ensure that everyone in your organization understands the pivotal importance of data cleaning by explaining that using raw data with a low signal to noise ratio limits the usefulness of any strategic insight they may produce. You’ll also want to research tools that can streamline the cleaning process so that you spend less time on it yourself.

Step Two: Enrich With Third-Party Datasets

Even companies with enough proficiency in data collection to build their own well-diversified proprietary information archives can’t collect every piece of data about every user. To develop the personalized insights that tech-forward marketing departments demand, you’ll need to seek out creative ways of enriching your data. Demographic and geographic databases are popular use cases, since they often overlap substantially with company data that includes addresses and ZIP codes, but you’re likely to produce more innovative results if you invest in third-party databases that cover detailed browsing information.

A note of caution: with the European Union’s new General Data Protection Regulation law in effect, be careful that any databases you select are compliant with those rules if your company operates in markets governed by GDPR.

Step Three: Analyze Your Data

Analysis is the reason that anyone collects any data at all! With a cleaned, enriched dataset, you can begin to draw useful conclusions. The type of analysis you choose hinges on the key performance indicators that are important to your company and the type of data available to you. Qualitative data is amenable to text-driven analysis or data mining, but it’s not useful for computational analysis.

Quantitative data, on the other hand, can be interpreted with a variety of statistical tools, and its conclusions amplified with the integration of diagnostic analysis, which concerns itself with the root cause of past trends; predictive analysis, which examines likely future outcomes; and prescriptive analysis, which uses these insights to inform possible solutions to a current problem. However you begin your analysis, most business applications will culminate in prescriptive analysis, as the value of data for companies is usually wrapped up in how it can guide decision-making.

Step Four: Show Your Conclusions Clearly

In the visualization stage, aesthetic considerations come into the mix, and the focus will be on deciding how to best showcase the data that’s most relevant to your organization’s present strategic concerns. Without visual aids, you’ll have a hard time concisely explaining the takeaway from your analysis. First, take a top-level look at the major trends in your data and select the information that’s most relevant to your key performance indicators.

You should also consider what data types you plan to present: Are the data ordinal, nominal, interval, or ratio? Organized nominal or ordinal qualitative data can often be presented in simple category charts. For quantitative data, you may need to put extra effort into designing audience-friendly charts that make detailed mathematical insight accessible. If you’re more of an analyst than an artist, check out these tips for making a great impression with your visualization design.

If your latest project is analyzing the petabytes of data your company has been holding onto for years, it’s still possible to make progress by ordering your approach around these four steps for making data more useful.

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