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Enabling Data-Driven Decision Making

Tracking and understanding the performance of your business is critical in competitive markets and growing companies are realising just how many answers can be found in their business data. The key question in boardrooms across the world has become ‘What are the numbers saying?’ … but getting to an answer is not always trivial.  

At Opti-Num Solutions, our team of data scientists, software developers and business consultants have been developing ways to understand what the numbers are saying for over a decade.  

In this post, we will show you how we typically make use of Opti-Nums Data Management Framework and PowerBI to process, model and visualise your big data into dynamic and insightful views tailored to your business needs.  

Opti-Num Solution’s Data Management Framework 

Data processing is a crucial first step in any business analytics endeavour. This is because business data is typically unfiltered and dirty, especially for novel analytics endeavours. Unfortunately, one of the greatest barriers to working efficiently is the need to repeat the same data processing task for every new analysis. The Data Management Framework (DMF) allows us to process data quickly and efficiently. Our procedure can be broken down into three steps:  

  1. Getting the Data 
  2. Managing the Data 
  3. Visualising the Data 

Step 1: Getting the Data

Getting the raw data for an analysis from a single data source can be a simple task once you have the right connections and credentials, but getting the right data to answer a business question from a variety of data sources is much less simple, and can quickly become a tedious process. This is because the data that comes from these various sources is usually big, unfiltered, and incompatible. Analysts often have to engage in repetitive, time-intensive re-aggregation, merging, and filtering for every new analysis, while their deadline approaches.

Opti-Num’s DMF provides pre-defined functionality to help our team quickly and efficiently pull and process raw business data from various sources into a form that is ready to be analysed. Practically, this means that our analysts simply need to point the DMF to the many data sources, specify the required levels of aggregation, and wait for the magic to happen.

Step 2: Managing the Data

 

The next challenge that analysts face is managing the data throughout the rest of their analyse process. The DMF helps us do this with its Data Management module. Once data has been processed, the DMF stores the data in an intermediate database that has been optimised for our analysis environment.

In addition to storing the data in an optimised environment, the DMF has another important data management task to perform that is based on a common challenge we face when working with agile businesses. In many cases, the raw data that the DMF has already processed and stored may be changed and updated by IT due to new business rules or fixes to data issues, and these changes can have serious effects on the accuracy of the analyses we perform.

To ensure that we are always working with accurate data, we incorporated a Data Processor module into the DMF. This module makes use of anomaly detection to monitor the completeness of our processed data and to perform updates when data parity is lost. The Data Processor module provides an additional benefit to the analysts by enabling them to fill in missing data through an array of different interpolation techniques.

Step 3: Visualising the Data

Attractive dashboards are critical for exposing actionable insights to executives who need to make business decisions. Consequently, the final step in any analysis is the visualisation of the data in order to communicate results (often in the form of Key Performance Indicators) back to decision makers.

In order to facilitate this, the DMF has functionality that enables us to integrate with various visualisation platforms and dashboards such as PowerBI. Dashboards such as these enable efficient distribution of functional reports that can be updated dynamically when the raw data is updated, and analyses are enhanced.

 

Conclusion

At Opti-Num, we are continually working with raw business data in order to generate actionable insights under tight deadlines. As data science specialists, we can’t afford to waste time performing repetitive tasks and so we have built tools such as the Data Management Framework that enable us to spend our time solving the tough problems that expose deep insights in data. The DMF has been a valuable resource in our consulting services and has provided us with a platform to use our expertise as data scientists to help businesses expose data driven insights.

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