Franco Primavesi | September 5, 2017

Don’t Let Data Quality Upend Your Big Data Analytics Endeavors

In the digital age, data analytics should be at the heart of any strategic decision making in business. Organizations have been throwing substantial amounts of data into their big data environments for years and they currently have all the data they have ever wanted. Now they just need to extract that data and put it to use by running analytics on it to make faster, smarter business decisions.

Any organization with mounds of data can benefit from its analysis. Analytics helps organizations understand their customers, perform their operations and make sound business decisions. However, organizations today are data-challenged with too much data and little information. The data is complex and not trustworthy and archaic tools are used requiring deep data science expertise. Not only is this data difficult to use, but errors can lead to inaccurate reporting. Business requests to the IT department for analytics tend to cause bottlenecks, and conflicting priorities across the organization hold data requests from business users on hold.

In order to pull analytics from a big data environment and run a successful analytics program, organizations need trustworthy data. They need a set of tools that the business user can easily leverage that doesn’t require IT expertise.

To ensure trustworthy data, organizations need a self-service, big data analytics platform designed to handle not one, but rather multiple steps from data acquisition and preparation to data analysis, visibility and operationalization. An end-to-end platform should include the following three components.

Data Governance

The first component any organization should look for in an analytics platform is data governance. It is easy to put data into a big data environment, but once in there, putting it to use becomes a challenge. Data governance allows businesses to easily define, track and manage all aspects of their data assets. This enables collaboration, knowledge-sharing and user empowerment through transparency across an entire enterprise.

The platform should deliver full transparency into all aspects of an enterprise’s data assets, from the data available, its owner/steward, lineage and usage, to its associated definitions, synonyms and business attributes. Full visibility allows business users to gain valuable insights into not only the details of their data assets, but the attendant risks associated with its use across business applications. Are you using the right data? With data governance you know upfront if you’re choosing the best data sources, if it can be used (e.g. licenses, privacy, regulations) and if those sources have quality data for your analytic project.

Data Quality 

The second component is data quality, which is imperative for improving analytical outcomes. Data errors are always going to happen, but what sets successful organizations apart from unsuccessful ones is how they deal with those errors.

Successful organizations usually implement a platform with high volume data quality and integrity checks such as data profiling, consistency, conformity, completeness, timeliness, reconciliations, visual data prep and machine learning to foster end-user trust by verifying the quality and integrity of an organization’s big data. This eliminates the need for multiple tools by aggregating data from disparate sources; pinpointing data of interest to perform data quality checks, aggregations and transformations; evaluate and review data quality; and combine and correlate data from different sources using a visual data prep process. Without a visual data prep process you’re back in the dark ages of coding data quality checks the old fashioned way. 

Analyze the Data

Once an organization has a data governance program in place and has checked their data for quality and integrity, it is time for analytics. Extracting insights without technical expertise can be very difficult unless an organization has the right platform, which leads to the most important component of a data analytics program.

The right platform should apply descriptive analytics and machine learning algorithms with intuitive drag-and-drop functionality to conduct ad hoc analysis, segmentation, classification, regression, recommendation and forecasting across many nodes for faster execution. This empowers the business user to access and control data in order to accelerate and improve the subsequent data analysis process, applying analytics to extract value from your data.

With the right platform in place, extracting data analytics becomes much easier, saves IT time and the organization money. Best of all, it gives business users the insights they need to make faster, smarter business decisions and increase overall revenue, with confidence.

To learn more about ensuring great analytics, download this data sheet.

Download the Data Sheet

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