Enterprises need to address the data challenges of the near future with a clear foresight about their application and architectural demands, and how data fits in.
The past decade and still, most of the enterprises aren’t looking beyond the transactional data, which technovert labels, the data beyond transactional as game-changer data.
There is an increasing demand to have a 360-degree view of the data to analyze and get the most valuable insights as a base for each decision a leadership or any executive to make daily.
This isn’t possible if the enterprises are still looking at the same transactional data and it is time for key leadership to start looking beyond and have a clear data strategy that is aligned with business goals.
For every enterprise dealing with data, there is an undoubtful need to develop a clear data strategy to ensure efficient and correct usage of the data that helps in discovering more opportunities for business growth.
Enterprises need to be more proactive than reactive.
Enterprise data strategy: How it is defined?
A data strategy should explain how data will empower and inspire business strategy. It is also about how a company will collect, store, manage, share and use data.
Every organization’s data strategy may not look alike. But In general, a data strategy will do the following:
- Define how data will help the company to meet its business goals
- Draft how the company will achieve the desired data activities to accomplish its objectives
- Outline the changes the organization needs to make to maximize the value of its data activities and plans for the same.
- Determine a timeline for completing the proposed activities, define milestones and priorities and describe a strategy for moving forward
- Discuss the commercial justification of the suggested data activities and how the company would benefit from them, and utilize insights to increase its profits and monetize its data
Here are five key areas to consider for building a perfect data strategy.
Earlier, fetching data from various ‘Transactional Systems’ and then ingesting, integrating them was the need of the hour. It still persists but the scope is not limited to that.
The emerging technologies will help ingest data from ‘non-transactional systems’ comprising of ‘Semi-Structured’ and ‘Unstructured data’.
A good example of ‘Semi-Structured’ data is ‘XML / JSON documents’ or ‘Other structure/pattern with no tabular format’ and for an ‘Unstructured Data’ is ‘Free Text’ (Documents, Tweets, Blogs, etc.)
‘Extract. Transform. Load (ETL) to Extract. Load. Transform (ELT)’ is the trend nowadays from structured to unstructured, whereas this is absolutely case-specific.
Key considerations to choose the right tool & technology choice are data type, volume, frequency, performance criteria, tech stack, scalability of the technology, Support for cloud and other architecture patterns.
Also, consider the question “What if the data gets lost while ingesting And How critical it is.
Once the data is ingested, it needs to be processed to serve a specific purpose. This might involve integrating with other information, applying business rules, cleansing, performing calculations or other
operations, or just storing them in a specific format for downstream applications and sometimes even ‘Do Nothing’.
All the above processing operations may need to happen in ‘Batches’ or ‘Near Real-time’ with minimal latency and ‘Real-time’ with no latency at all.
The data ingested and once processed need to be stored for its further usage based on the specific need.
This can be fed into another application(s), Analytics and Data Science. The type of storage architecture is dependent on its application. The storage location might be On the cloud or On-premise and the architectures shall vary from Conventional Data warehouse to Columnar DW, No SQL (Document, Key-Value Pair and Wide Column Store), Data Lake.
It is all about how you consume the processed data from the above stages. Once we have data available from both transactional and non-transactional source systems the ‘Data analysis’ operations which can bring more value through better predictability and recommendations.
Data Science essentially augments the whole ‘BI & Analytics’ solution and transform an organization’s decision making from ‘Diagnostic / Descriptive’ to ‘Predictive & Prescriptive’.
The above five elements, Data ingestion, processing, storage, and analysis are the key areas to be considered in a broader perspective while designing an enterprise data strategy.
Having all set and enjoying the derived insights from large data sets will go smoothly, only if a perfect Governance strategy is also in place. The data governance strategy should focus on the security and quality of the data to ensure a seamless delivery.
Where to start:
A good start point is the identification of your business needs and a particular performance Indicator you want to transform with the help of insights from data. Thus you should be able to focus and derive the required data characteristics by strategizing your data architecture accordingly.
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Data ingestion: the first step to a sound data strategy | Stitch resource
How to Create a Successful Data Strategy