Data is the new oil for today’s enterprise. Each day the amount of data generated by organizations grows exponentially. By harnessing knowledge from data, enterprises grow to make better data-driven decisions and rule over their competitors in the market. As such, massive amounts of time, energy and money is invested in managing the data.
To extract these insights, however, data needs to go through an analytic process of ingestion, cleaning, transformation and calculation. Only then can this data be analyzed and operationalized. Each step in the process has its own essence, and according to recent survey, 71% of enterprises have no comprehensive way of managing each step in the analytic process.
If data is the new oil, analytics is the gasoline that drives the business forward. By not actively managing analytics, enterprises are putting themselves at risk. Let’s take a look at two of those risks: orphan analytics and analytics drift.
Risk 1: Orphan Analytics
Orphan analytics occur when turning raw data into an isolated format (think Excel) to make conclusions about information. Orphan analytics quickly become irrelevant, as they aren’t engineered for collaboration, sharing, reuse, continuous-learning and adaptability to dynamic changes.
Data analysts and data scientists often waste resources recreating analytics from scratch in separate tools instead of making it easy to reuse an existing set. It is imperative to identify early signals indicating the trap of orphan analytics. This occurs due to lack of business planning and scarcity of requirement prioritization. It is a waste of resources and knowledge when analysis is done from scratch rather than following a defined process to reuse existing analyses whether a simple data transformation or a sophisticated machine learning model.
Strategies to Prevent Orphan Analytics
- Data and analytic professionals should plan and work closely with their customers to define the problem statement and discuss how the analysis will be used and deployed
- Implement the DRY (Don’t Repeat Yourself) rule. Create a process that makes it easy to discover, understand the definition of, and reuse analytics across your entire team, department and enterprise. Make sure to document the problem statement the analysis is trying to answer, providing context to others that will reuse it.
- Write tests to check for anomalies and inconsistencies across data and analytics that are trying to answer the same problem statement.
Risk 2: Analytics Drift
Business strategies and models are ever changing, and analysis of the data is essential to make data driven decisions. Thus, analyses needs to be updated in lock-step with changes in the business environment. Failure to revise analytics to keep up with changes will lead to “Analytics Drift”. Analytic drift occurs when the data and analytics teams fail to accurately reflect and measure what their customers are currently trying to accomplish.
Analyses and calculations can easily become irrelevant over time, as the seasons change, the market evolves, and/or business strategy takes a new direction. Failure to update the analytics framework with respect to inevitable change creates analytic drift. An analysis done for the holiday season may not reflect day-to-day business due to higher customer demand during the holidays.
Analytic drift erodes trust in an organization’s analytics, leading to:
- Inconsistent use of data and analytic resources
- Erroneous business decisions
- Increase time spent trying reconcile data and analytic output
- Degradation of machine learning or artificial intelligence model performance
Strategies to Prevent Analytics Drift
- Build analytics which can easily be updated, track changes with version control, and be reused across several use cases
- Define and document what analytics should be used for, by whom they should be used, and for what purpose
- Create regularly scheduled tests to evaluate whether the analytic is measuring the desired outcome on a consistent basis
- Identify which analytics are most prone to analytic drift based on past performance
Process and tools for mitigating these risks
Aginity empowers data/analytic engineers, data scientists, and data/business analysts with field-tested capabilities to help guard against orphan analytics and analytic drift.
Solid analytics must go beyond eye-candy IDE’s, dashboards, and visualizations. A solution to orphan analytics and analytic drift can only be found within a corporate culture which values analytic transparency and collaboration.
Both Aginity Premium and Aginity Enterprise support such a culture. Our innovative shared catalog gives engineers and analytic stewards the power to curate, manage, and promote the most useful & efficient SQL assets for wider use. These analytic assets can be referenced as modularized objects to ensure execution of the latest version within live queries.
Aginity works to narrow down the gap between the accurate analysis and the raw data of an organization, minimizing the analytic mismanagement by connecting an organization’s SQL community to reuse and share consistent analytics results. To learn more, schedule a demo of Aginity today.