5 Best Data Management Practices to Help You Make Data Right

By Cindy Turner, SAS Insights Editor

If you deal with virtually anything, you have some important data hanging out with your business. In fact, you probably have one lot of important data in one lot of different places – internally and externally. What you may be missing is the best practices for data management that can help you get to all this data and look into it. Doing just that can give you a glimpse of insights that can push your business into a brand new market or send profits higher than all expectations.

But what, and where, is IS all the data relevant to your business? Can you access it when you want it? Do you know that it is accurate, topical, clean and complete? Can you easily pull all the data together, no matter what format they are in or how often they change?

The big question here: Is your data ready to support business analytics? An often ignored truth is that before you can do really exciting things with analytics, you must first be able to “execute” data. Data management, that is.

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Data management best practices = better analytics

Of course, plenty of companies have been analyzing data that was not really prepared for analysis. Their data may have been incomplete – perhaps the enterprise infrastructure could not accommodate any new data format, such as unstructured data from text messages. Or maybe they were working on duplicate data, corrupt data or outdated data.

Until these companies find a better way to manage their data, the results of their analysis will be something… well, less than optimal. So how difficult is it to manage unfiltered data and get it ready for analysis? Ask a data scientist. Most of them spend 50 to 80 percent of their model development time on data preparation alone.

5 best practices for data management to get your data ready for analysis

  • Simplify access to traditional and new data. More data generally means better predictors, so bigger is really better when it comes to how much data your business analysts and data scientists can get their hands on. With access to more data, it is easier to quickly determine which data best predicts a result. SAS helps by offering a plethora of built-in data access features that make it easy to work with a variety of data from ever-increasing sources, formats and structures.
  • Strengthen the data scientist’s arsenal with advanced analytical techniques. SAS provides advanced statistical analysis functions inside the ETL stream. For example, frequency analysis helps identify outliers and missing values ​​that may obscure other measures such as mean, average, and median. Summary statistics help analysts understand the distribution and variance – because data is not always distributed normally, as many statistical methods assume. Correlation shows which variables or combination of variables will be most useful based on predictable capacity strength – in view of which variables can influence each other and to what degree.
  • Scrub data to build quality into existing processes. Up to 40 percent of all strategic processes fail due to poor data. With a data quality platform designed around data management best practices, you can integrate data cleaning directly into your data integration stream. Pushing the processing down to the database improves performance. It also removes invalid data based on the method of analysis you use and enriches data via binning (i.e., grouping data that was originally at smaller intervals).
  • Form data using flexible manipulation techniques. Preparing data for analysis requires merging, transforming, de-normalizing and sometimes merging your source data from multiple tables into a very wide table, often called an analytical base table (ABT). SAS simplifies data transposition with intuitive, graphical interfaces for transformations. Plus it allows you to use other reshaping transforms such as frequency analysis, data addition, data splitting and combination, and multiple summarization techniques.
  • Share metadata across data management and analytics domains. A regular metadata layer allows you to consistently repeat your data preparation processes. It promotes collaboration, provides descent information about the data preparation process and makes it easier to implement models. You will notice better productivity, more accurate models, faster cycle times, more flexibility and revised, transparent data.

Data: The basis for decisions

Analytics is probably one of the hottest IT issues around these days – it’s undeniably very sexy technology. But as you dream magic of analytics, keep this in mind: Underlying analyzes are data. Don’t underestimate how important it is to make your data right.

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