By Deepak Mittal , Sidharth Mittal
In today’s agile fast-paced world, “Data” is the king. Actionable and insightful reports using unstructured and structured data from multiple sources can drive organizations towards their Key Business Imperatives, Identify New Opportunities, and making Evidence-Backed Decisions. While organizations have invested a lot of resources in analytics projects, the success rate of these projects is low. As per our study, the following are seven main reasons for an analytics project failure. Our analytics solution Best Practices avoid these pitfalls and delivers value.
1. Missing Data and Poor Data Quality (Poor Data Handling)
Poor data handling can lead to incorrect and invalid results that can lead to making sub-optimal business decisions. Here are some of the challenges:
- Manual Data Management: When data is coming from multiple sources and manual interventions/ steps are too many, it loses its credibility. Systems with high manual interventions lead to potential data manipulations, data loss and poor record keeping of the data set.
- Insufficient Error Handling: Inadequate data error checks and limited error notifications let poor data pass through. This poor data may lead to poor business decisions and reducing the ROI of your analytics project.
- Insufficient Data Quality checks: While transforming large volumes of data, insufficient data accuracy validations and standard transformation may lead to various data issues such as duplicate records, blank value, Inconsistent or incorrect formulas, incorrect formulas (division by zero handling).
- Gap in Data and missing attributes: Missing or limited data either by time or by units or by dollar value or by attributes lead to an incomplete story. Insights from such data are inaccurate and should not be used for business decision making. In such cases, lessons are not learned from previous period data and the same mistakes may be repeated.
2. Unmanaged Analytics Solution
- Data Security and Database performance: While creating analytics solution, many organizations dive straight into analytics and do not set up data security and database performance measures that puts organizations at risk of internal business conflicts and data breach.
- Roles and Permission: Often organizations do not spend enough time and effort in defining roles and permissions to data set at row level, user level, and report level, resulting in exposing privileged information.
- Duplicate reports and data sets : Many times reports and data sets are copied and create redundancy of data and reports.
3. Poor Visualization Standard
A very big cause of analytics project failure is, when visualization does not highlight actionable insights, and forces executives, managers to spend time in deciding actions to be taken based on their experience rather than data. Here are the some of the challenges:
- Too Much Data: Visualizing too much data in tabular format gives summary of data but does not tell the user, how to interpret this data and make informed decisions.
- Busy Dashboards: Having dashboards with multiple charts and graphs but lacking a story or relation among charts.
- Too Many Options: Several filters and options on each dashboard means dashboard designer has left all the insights finding work to the viewer.
- Summary data to detailed data: Inability to connect multiple reports using drill-down functionality for end users to be able to justify the analysis at a granular level.
- Undefined visualization standards: While creating visualization if standards are not followed, insights may not be obvious to the users.
4. Missing Insights
When Insights, KPIs , benchmarks go missing from any analytics solution it cannot be successful. There could be one or many of the causes for missing the insights
- Failure to Benchmark: Failure to use Industry KPIs in charts can lead to the failure of the analytics objective.
- Not Comparing and Measuring: Missing time-based comparisons to measure and assess business performance can lead to failure as well.
5. Missing Opportunities and Trust
A timely insight can save time, effort and add to the top line. That opportunity is lost if insight is missing in charts. Executives and managers need to be able to trust the numbers provided to make decision. The following are some of the cases that if not managed can lead to the failure of an analytics solution.
- Decision based on Historical Data: Decisions being made off of historical data are like a lost opportunity to improve the performance and efficiency of the business.
- Business driven by assumption: When business decisions are run on assumptions when faulty can lead to a drop in business value.
- Lack of Trust: Trust is a very important factor in any successful analytics solution. When analytics solutions are not able to prove the story is correct and credible to make decision it fails.
6. Missing or Inaccurate Predictive Analytics
One of the biggest reason business initiates and invests in analytics solutions is to be able to predict the outlook of your business with greater certainty and minimize the risk. When Analytics solutions do not support predictive models or models have lack of confidence it loses its ROI.
7. Poor Report/Dashboard Performance
High response time to be able to view your analytics can be the cause of any solution to fail. Analytics solution slow down can be caused by multiple factors such as unsound schema architecture, unhandled null values within data attributes, too many reports or not breaking data into smaller data sets to consume.
For an analytics solution to be successful for any organization, a focus on the above seven key aspects are needed. If any of these key aspects are missing, it can lead to the failure of a business that relies on analytics solutions. Remember, data is the king. For an analytics solution to succeed, it is important that the data is correctly cleaned and structured, delivered in a timely manner, and correctly computed and visualized. All this will drive the business through any market condition.
