Transforming raw data into a format that can be correctly utilised and incorporated into Business Intelligence processes is a fundamental step in gaining access to data-driven insights.
Poorly formatted data with a lack of governance can only offer limited intelligence, add the challenge of multiple data sources to this and the limitations are increased further. Not just reducing insight, but strategy, and the potential success of enterprises in the process.
As a Business Intelligence consultancy, we’re committed to empowering data analysis and BI efforts, enabling businesses to gain the most value possible from their data. Through transforming raw data into actionable insights, we can ensure data quality, and ensure that all types of data can be utilised in analysis.
What is raw data?
Put simply, raw data is the term given to describe collected data before it has been processed, formatted, and stored in a Data Warehouse. Without the correct transformation, this data can offer limited scope and insight, and may not be of use in BI and Analytical tools
This is due to two core factors: the lack of compatibility, and possible limitations in quality.
Lack of compatibility in raw data
Source data that has not been correctly transformed and formatted will not be compatible with a range of Business Intelligence and analysis tools. As such, vital information and overall insight will be limited, leading to a range of challenges within the BI solution.
A lack of refinement will cause challenges to Machine Learning models with problem data skewing outcomes. Critical data relating to financial planning & allocation may be biased – causing challenges to important business decisions; along with many more potential challenges.
Limitation in quality
Without the correct governance and processing procedures in place, there is no guarantee that collected source data will be high-quality and consistent – reducing overall trust in intelligence.
Another consequence of the overall lack of data quality is a potential reversion to manual approaches – a viewpoint that may lead to data silos and a decrease in productivity; resulting in the opposite of what a business was trying to achieve in the first instance.
How can raw data be transformed?
To ensure that raw data is transformed into formats that can be utilised effectively by BI tools and more, we recommend implementing both streamlined ETL\ELT processes, as well as applying enterprise-wide frameworks for correct and consistent data governance.
ETL, or Extract, Transform, and Load, allows for the transformation of raw data into a structured format, before migrating datasets into a Data Warehouse for use in analysis and BI efforts. ETL tools will differ depending on the unique architecture and frameworks of each enterprise, and so will require a specialist approach. However, once these processes are introduced, they offer a sophisticated and efficient approach with very little maintenance.
ETL processes involve three core phases, not necessarily applied in this order. Sometimes, data will be transformed as it is moved from its source – ETL, and sometimes it will be transformed after it’s been loaded in its raw state – ELT. Both are valid approaches depending on the specific use case.
Within the extraction phase, raw data is collected from different sources. Given the breadth and complexity of business solutions today, the extraction process will employ multiple techniques and technologies to retrieve data. Fetching data directly from databases, consuming API’s or Streaming Data, and loading Flat Files are just a few examples.
One of the main objectives for the extraction process is to pull the differential data where possible – that’s new and changed data only, and to only extract what is needed later in the process. This approach should minimise the impact on the systems that data is being extracted from, and make processes quick and efficient by minimising the data being collected. This is essential as we witness the need for near real-time demands on data increase.
The transformation phase is perhaps the most critical stage within the ETL\ELT process – and is where the enterprise can apply business logic to data to ensure that it is high-quality, consistent in format, and able to provide the desired insight.
Data can be transformed at one or more stages in processes. In a typical ETL process, data is transformed when it’s in-flight as part of the extract process, as well as when it is held in a staging area. This ensures that data preparation happens before data is then loaded into its final destination – which could be a Data Warehouse or Data Mart of some type.
Data can also be transformed further after it has been loaded ELT – adding to and enriching data in its final resting place. This is common practice when transformation processes need access to all data, not just the differential data coming through. There may also be ad-hoc transformations created by analysts in their exploration and use of data.
This need for a customised approach to transformation requirements is one of the core reasons that ETL\ELT processes must be configured for each enterprise, alongside other demands based around the technology stack and storage options. The demands on data that supports a set of standard reports and analysis when compared to data used by analysts for mining and modelling may have very different needs.
The load stage in the process is responsible for putting data into the target data storage location. This could be a Data Warehouse, Data Lake, Customised End-Point or one of many data load options. This process may have different phases, such as loading data into a staging area, and then loading data into a final resting place.
Load processes will typically be audited and monitored to ensure they are efficient, don’t impact in-flight analysis wherever possible, and are proactive in reporting problems with processes. Wherever possible, load processes will be transactional and repeatable to ensure they are robust and enterprise-ready.
Through these three stages, enterprises can ensure that source data collected will become actionable, meaningful and trusted – giving the business insights that are based on reliable, secure and validated data that reflects transformations based on business logic.
There are many tools available for introducing ETL or ELT processes into an enterprise’s architecture – DataShapa have several frameworks that can leverage the tools and technologies that underpin these processes. However, while these tools can enable rich ETL\ELT capabilities, care must be taken to ensure that processes are optimised and cater to the specific architecture and frameworks present.
For a truly optimised and well-informed approach for integration, enterprises can use the services of specialised teams. Our market-leading ETL process ensures that all data sources are accommodated and maintained accordingly.
The inclusion of Data Governance
In communicating a clear and consistent governance approach with collected data, internal teams can ensure that all data continues to be maintained, accessed correctly, and secured to prevent instability and possible breaches.
With a correct approach to data governance alongside a data-driven culture, enterprises can guarantee that any data that has gone through the ETL process will be stored in a future-proofed solution for on-demand use.
To learn more about the importance of a data-centric culture, read our blog here.
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