analytics knowledge management

Analytics Knowledge Management – Why does it matter?

  • Benoite Yver
  • February 21, 2020

In today’s world, business decision-making benefits from the availability of massive amounts of data and a wide range of analytical tools to derive insights from it. Tools and automated processes for capturing, storing, and preparing data to provide access to torrents of data that organizations can leverage to drive business decisions.

However, the wealth of data and the speed at which it is growing has only exposed daunting challenges in gleaning meaningful insights for decision-making fast enough to be actionable. Machines can do the grunt work of data plumbing and generate automated insights with the advent of AI. However, the human element still plays a significant and indispensable role in developing value out of data.

The Typical Process of Running an Analysis

Extracting insights from data is an iterative process of discovering and exploring data from multiple sources, blending and transforming it, and analyzing it to find relationships and patterns that can be made actionable. This process often involves collaborating with other analysts, data scientists, data engineers, and subject matter experts to access the right data, understand it, trust it, and leverage it in the analysis.

During this iterative process of data discovery, data preparation, collaboration, and analysis, raw data goes through several transformations and is augmented with knowledge and expertise from across the organization to analyze and interpret the results to make actionable recommendations.

In most organizations today, all the knowledge that goes into an analysis typically never gets formally recorded in an easily searchable and reusable form in the future. Such experience includes any of the following and more,

  • Data transformations including filtering of outliers, sampling, calculations, blends, etc.
  • Data interpretation, which generally involves working with the data owner or subject matter expert
  • Queries, statistical models, and code
  • Documents
  • Results, recommendations, reports, and dashboards

All this knowledge, along with the data itself, constitute the Analytics Assets that an organization possesses. In the inability to capture these in a searchable and reusable format, the assets are prone to get lost or buried in places like an analyst’s laptop or memory. What happens then if an analyst leaves the organization? All the created knowledge is nearly lost. The inability to reuse experience and analytics assets invariably leads to duplication of work and reinventing the wheel.

Benefits of Analytics Knowledge Management

Organizations build analytics knowledge over time. Created experience is with every analysis, every new data source, and every business question that is asked and answered by leveraging data. Here are some of the benefits of robust analytics knowledge management.

1. Reuse of assets for a shorter time to insights

Consider an analysis that combines two siloed datasets, one containing transaction data and another containing customer data associated with those transactions, to gain insights on purchase patterns by demographics, geography, or other attributes. Creating this blend of two datasets involves discovering the two datasets, understanding their data, and what column to blend them on. Now consider another business question that comes in at a later point that seeks to understand purchase patterns by various customer attributes during periods or certain weather events, which is the third dataset. While the analyst is trying to answer this question, he could search and find the previously created dataset using transaction and customer data. Their analysis could reuse this dataset and only blend in the new weather data with it.

Without knowledge management, or, in effect, without access to this previously done analysis, the analyst would have to discover all three datasets from scratch, understand them, and blend them to use in their research. Having to redo work previously done naturally increases the time required to answer a business question, which can negatively impact the agility to make data-driven business decisions and, consequently, on business performance.

2. Completeness of Analysis – solving for the Unknown Unknowns

Consider the same example as above, where an analyst blends in weather data with transaction and customer data. It is not uncommon to have 3rd party data, such as weather data, in an external vendor extract. The same holds for data that may come from a non-directly connected source to the analyst’s BI or Data Science tool of choice. It may be available in an easily accessible data warehouse, thus forcing them to use an extract of it from another system or SaaS application.

Suppose this data and the knowledge is not captured in a centralized, searchable format. It is quite likely that another analyst, who is trying to answer a question that could have benefited from the use of this data, may not even know that such data was available or think about leveraging such data to answer the question at hand. It could lead to an incomplete, possibly misleading, analysis, and recommendation.

3. Fostering Collaboration for knowledge and expertise sharing

Collaboration between analysts within the same team and those across business functions can lead to better analytics driven by knowledge and subject matter expertise. A question that analysts frequently have is who to reach out to if they had a problem about a data source, a column within a table, or how a specific analytics asset can be used in an analysis. In a world without analytics knowledge management, the process involves emailing many people or walking around the office asking co-workers who might know the answer. With a comprehensive analytics knowledge management solution, the analyst could search for available data or analytics assets, and the system would not only list them out. They could also provide information about who owns the data, what projects it has used in most recently or most frequently, and who the analysts own and use it. The analyst could then reach out to the right person within minutes of the search and start collaborating. Leveraging the knowledge and subject matter expertise of colleagues who own or have worked with an analytics asset before can significantly accelerate the time to insights and provide invaluable inputs for a better, more comprehensive analysis.

How does Datameer Spotlight enable Analytics Knowledge Management

Datameer Spotlight is a SaaS solution that offers extensive, AI-augmented analytics knowledge management capabilities. These let analytics teams build knowledge over time and access it seamlessly for use in future analyses, without ever requiring to duplicate efforts or trying to figure out where, or with whom, the knowledge resides. Datameer Spotlight also enables collaboration between analysts by letting them work together on an analysis in, what we call, a workspacewhich is a collection of all analytics assets required to answer the business question at hand. Learn more about Datameer Spotlight’s knowledge management and collaboration capabilities by scheduling a 1:1 Demo.

In addition to Analytics Knowledge Management and Collaboration, Datameer Spotlight also lets you connect to any data source virtually. On-premises or in the cloud, it blends and transforms data to create new data assets without having to worry about the physical location of the underlying data sources and consume them in any Business Intelligence or Data Science tool of your choice. Click to Learn More.

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