Overcoming Data Preparation Challenges

Steve Ludwig Data Management

Organizations that understand the distinction between reporting and analytics simultaneously innovate in data quality and analytics. Analytics needs data, but analytics programs are not necessarily reporting departments. Reporting is a process of organizing data, whereas analytics is a process of exploring data. Analytics is forward-driven, answering what will happen. Reporting usually looks at what has already happened.

According to the Harvard Business Review (HBR), analytics outcomes are not matching analytics spending. HBR cites data integration efforts and lack of talent as primary reasons. Data preparation often hinders the growth of analytics programs. Furthermore, according to McKinsey, organizations are wasting money on analytics efforts. There is a tendency for organizations to thoroughly clean data before starting analytics.

According to Gartner, there is a $1B data preparation market ready to satisfy the needs of organizations looking for perfect data. Tableau now offers “Tableau Prep” to assist in data preparation. AI software such as IBM Watson has data cleanup features And, Microsoft has a PowerQuery tool to help analysts aggregate data.

The solutions falling into the $1B data preparation market may not provide the right path towards analytics once the data is clean. These software tools are focusing on data cleaning, exacerbating the trends noted by HBR and McKinsey. Analytics efforts fail because organizations become stuck in data cleaning and data preparation. As more organizations become stuck in data cleaning and data preparation, more products choose to focus on this direction, versus analytics.

To overcome this cycle, data preparation should occur in tandem with analytics efforts. Furthermore, analytics should happen without high fixed costs. A platform such as Lityx can help organizations escape the ‘perfect’ data trap and achieve operational excellence in analytics. Here is how: On the data management side, Lityx uses advanced data aggregation features. These features help marketing analytics departments generate insights from transactional data. One often overlooked approach is “windowing” or “partitioning” functions.

These approaches excel at answering the following types of questions:

1.) How long between marketing activity and purchase?

2.) What’s the total dollar value of purchases by customer X?

3.) Is the customer spending at an increasing or decreasing rate?

4.) What regression fits the purchase behavior of customer X?

An example of windowing is below:

Data management is a sound investment to any analytics strategy. The graphical interface adds simplicity, and lets analysts execute complicated queries. With Lityx DataManager, marketing analysts gain deeper analytical insights with fewer database queries. Using a graphical interface for data preparation helps analysts uncover new patterns in transactional data. Instead of writing specifications and tickets, analysts can compute multiple scenarios using the GUI and using their knowledge of statistics. This approach minimizes the burden on IT departments to run queries and the development of “specifications” which can slow the analytic process.

Lityx excels at data management while simultaneously encouraging progression in the analytics development cycle. Once transactional data is aggregated, analysts can move to the other modules in Lityx which include Insight, Predict and Optimize.

Step 1 – Creation of one aggregation field.  This groups your calculations by Customer ID and orders by Transaction date.
Step 2 – Choose statistics to compute, based on the segmentation grouping above.   Multiple statistics are created in one query, without any ‘coding.’

The result, a clean data set!

A strong analytics program improves insight while at the same time innovating in operations and data quality. Data preparation is a very important (and overlooked) aspect of any marketing analytics program. Bringing the power of database analytic queries to analysts simplifies the process.

Learn more about Lityx, here.

Steve Ludwig is a Senior Consultant with Lityx, LLC.  At Lityx, Steve helps clients with a variety of marketing measurement, customer analytics and prospecting analytic initiatives.  He earned his M.S. in integrated marketing from Northwestern University and has a B.S. in economics from the University of Michigan.   Steve can be reached at sludwig@lityx.com.

Ready to get more out of your data? Talk to an analyst to learn how Lityx can partner with you to help you assess your data management and leverage advanced analytics to improve your marketing efforts.

Also see Non-Profits: Work Smarter, Not Harder.

 


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