1. The Data-to-Impact Journey
1.1 Raw Data :
Originating Point Raw data is crude information gathered via different sources- CRM databases, ERP systems, sensors, Web logs or customer surveys. Raw data on its own is: Unstructured (e.g. text, audio, video) or structured (e.g. numeric tables) Frequently straggler, patched, or redundant No context to directly make decisions It is not that one wants to aggregate more data, but rather quality data that can be converted to practical application.
1.2 Insights :
How to Find the Signal inside the Noise ? An insight is a deduction made through the analysis of data that will identify a trend, relationship or an opportunity. This requires:
- Cleaning & preprocessing data.
- Machine learning algorithms & statistics.
- Graphical analyses that will help to uncover tendencies.
Unclean data: 10,000 purchase records of customers.
Findings: The ideal consumer group who are likely to make purchases during weekend flash sales are between 25 to 34 years of age and reside in urban locations (who are 40 percent more likely to purchase than those who reside in rural areas in cases of flash sales taking place during weekends).
1.3 Impact Change :
Driving Decisions impact occurs when insights are converted into knowledgeable decisions, the knowledgeable decisions are used to yield concrete result- revenue growth, cost reduction, effectiveness, or customer satisfaction.
Example: With the previous understanding, an e-business organization initiates weekend campaigns that can attract urban millennials- resulting in a 15 percent sales increase within a single quarter.
2. The Challenges of Turning Insights into Impact
2.1 Too much Data :
An excessive amount of the data without the prioritization might burden the teams and stall the decision-making process.
Statistics : Gartner discovered that between 60 and 73 percent of enterprise information lies unused as an input to analytics. Siloed Data Systems.
2.2 Siloed Data Systems :
In case marketing, sales, operations, and their customer service departments have individual data systems:
- There are insights in bits and pieces.
- Collaboration suffers.
- There is a failure of a comprehensive approach to the selection of strategies.
2.3 Deliberate Decision making :
Though actions may not be delayed in spite of excellent insights, bureaucracy tends to slow actions resulting to missed opportunities.
2.4 Weak Data-literacy :
Without the ability required by the decision-makers to understand the output of analytics tools, its insights cannot be converted into a viable strategy.
2.5 Bias and Misinterpretation :
Speaking of imperfect data or misinterpreted patterns, these may cause inappropriate decisions, destroying analytics credibility.
