Data Transformation & Stitching

Big data is no more just a buzzword in tech circles but it is actually being actively used by organizations to solve the challenges they face in their business operations.

Consider these:

  • Between 2015 and 2020, the total data will grow 10x
  • By 2020, global IoT market is projected to grow to $457 billion
  • There will be 6.1 billion mobile phone users
  • Less than 0.5% of all data is ever analyzed and used

The 3 Vs of big data – Volume, Variety and Velocity – of data is growing at breakneck speed. If the businesses realize the startling fact that today they are gaining insights from less than .5% of this data, one can imagine what would happen if they don’t prepare themselves.

Irrespective of what sector the companies are in, they must have a data architecture plan and must master the art and science data transformation. 

Also known as ETL (Extract/Transform/Load), the data transformation is the process of converting the source raw data into a ready-to-use form. This can then enable processes that can turn data into timely insights that positively impact businesses. It is proven that only appropriately transformed data, that is easily understood by the business users, is seen as a trustworthy source of insights.

With the ever-growing volume of data available to and about your business, you have a great opportunity to use it to find new business value. But harnessing this data requires a conscientious data transformation strategy that orients the data around the needs of business users.

Our Approach

Start with Goal Post in mind

Organizations have enormous amount of diverse data both internally and externally. It is easy to get lost in this ocean. Hence, before transforming data into insights, we engage business users to understand the business processes that they want to analyze, and design the target format.

Analytics

Organize as per Dimensions

Each company has certain dimensions to put context around the facts and explain what is happening within those dimensional context. Example of dimensions are customers, products and dates to analyze sales results and measures as facts.

Target Format

Transform as per the Target Format

The target business process drives the transformation of source data to the target business process and the data team automates the data flow for successive data loads. The process breaks down the silos between data and frees up analysts for more value-added work.

Project Management

Continuous Engagement

The measure of the value of data transformation is the extent to which the target processes use the transformed data asset. Making data available to the end users isn’t the end of data transformation, it is the first step.

Procedures

To understand the transformation needs of your company, you need an on-going, professional review to manage your data opportunities and risks. Some examples include:

  • Data profiling to record and understand the state of the raw data your company possesses. This would then be followed by a scope assessment for the amount of work needed to perform on it to make it ready for analysis. This helps companies to get to know their data before transforming it.
  • Prepare a data dictionary and master data inventory to know the size of data sets the features, the data types, the relationships between data, the feasible range of values, the frequency of missing data, the junk data, and the size of data. 
  •  The source data formats would need to be harmonized and transformed to match the target format. If some data have a large frequency of missing values or junk data, then either they would be replaced by estimated values for missing data or be excluded
  • Having a clear policy of data cleansing early in the data transformation process helps ensure that the obviously bad data does not make it to final analysis, reduce noise and errors which ultimately improve confidence in the output of the data

  • Have an audit trail for the data to ensure that it is possible to “work backwards” and answer common questions like “I don’t think that this data is correct” or “Where did this come from?” or “How do I know this KPI is reflecting facts correctly?” Having an ability to quickly audit the trail and have ready and reliable answers builds user confidence

You can engage us for any or all functions, as we provide the entire array of services.