How to build a lasting friendship with your data through access, data quality and tools.
Data from direct selling software has been a friend of mine for decades now but not without challenges. We have had our ups and downs as I have encountered issues with data access, data quality, and the advent of reporting tools. Data can be your friend too.
There is little argument regarding the potential of data. Its most straightforward use amounts to a tally of sales units or money, answering the question How much did we sell? Yet data provide valuable insight into forecasts, predictions and trends. With the right access, data quality and tools it is conceivable to not only tally sales but predict how much will be sold between 6:00 pm and midnight on Friday. The opportunities are endless—with close attention to access, quality and tools.
Access to your data is a key quality to the timing and reliability of your new friendship. Data begins as a collection of data dimensions or categories such as product types or customer types, followed by transactions like a product receipt, a new customer or a sales order. These data seemingly disappear into a labyrinth called a database. In more complex systems, there may be synchronization of data across multiple databases.
Access to these data or databases is paramount to building the new friendship. Access to only a library of reports is akin to a casual acquaintance. A meaningful relationship can only be attained with permission granted to access all of the data.
“Garbage in garbage out” (GIGO) is a classic simplification of the data quality issue. Quality is key to a solid production of reliable data results. The phrase GIGO clearly points to the culprit. Data validation or policing the quality of the data during entry is key. The simplest example is represented by the age-old problem of misspelling a customer’s state. FL should be the only valid entry for Florida and not FLA.
However, data validation is more than a list of acceptable values. Programmers must be keen to address computations or algorithms with multiple variables to guarantee accurate results. Data architectures must enforce data integrity to ensure that all data relationships are valid. Consider the issue of a Sales Order without a valid Customer.
There are many data analytical tools available in the marketplace today. The complexity of data export and migration, however, can cause unforeseen issues requiring competent data analysts. Additionally, a new reporting environment may require technical assistance and training. Consider that even a data export to Excel can alter data in ways that affect your results.
In the absence of a budget for analytical tools, programmers and analysts, it is best to rely on reporting tools provided with your Direct Selling software. A directly embedded reporting tool aids in avoiding issues regarding access to data and export challenges. Add a well-architected Direct Selling database with adequate data validation rules, and GIGO is solved too.
Build a lasting friendship with your data through access, data quality and tools. Ensure that you have unfettered access. Be certain that your system enforces data quality and look for systems that have a rich set of reports and tools to provide the freedom to get to know your new friend.
David Kelley is the Senior VP of Business Development for IDSTC. David has over 30 years of IT development and Data Analytics experience, with most of those years in the Direct Selling business vertical.
From the May 2021 issue of Direct Selling News magazine.
