Data is everywhere and it powers everything we do!
In this article I would like to focus on how companies can start their data-centric strategies and how to achieve success in their data transformation journeys. Have tried to share my thoughts why companies have to consider data at its epitome for their growth, for being competitive, for being smarter, innovative and be prepared for any unforeseen market surprises.
By Pradeep Adaviswamy, Regional Manager of Analytics at Bahwan CyberTek
Our Mother Nature has provided 5 great elements or resources i.e., fire, water, space, air and land and now we need to add Data to this list.
Nature also deals with its own data set through DNA and moreover it is organically organised. Now it is our responsibility to make use of data that we have effectively and efficiently.
We are all generating data in abundance and now organisations are facing the challenge to curate and monetise the data for becoming competitive in market, for their survival and for bringing in innovation, profitability into their products and services.
In this article I would like to focus on how companies can start their data-centric strategies and how to achieve success in their data transformation journeys.
Some organisations have the right ingredients(data) and they need to come up with right insights for their benefit and some organisations have the right use cases and they need to find out the right ingredients/data to address and accomplish the use case. Both approaches work and all it depends is on the overall success of their data strategy to meet their end business goal and purpose.
Data monetisation is the new global economy and organisations old/big/small or start-ups will have to capture, store, process and consume intelligently. This has to be a continuous feedback loop data cycle. There is a plethora of tools and technology which can address this data cycle but the success depends upon organisation's direction and governance.
Industry study and field experience says never to go with a big bang approach ('think big and start small') and always have an interim check points while establishing enterprise data program. Organisations should not think that having a big data/data lake setup or AI lab will solve all their analytics demand.
It will be prosperous if companies have a data-centric architecture approach with overall governing body in place else, just by having data lake/hub is not taking companies anywhere. Data lake will have to be maintained, curated, secured etc. It should not just treat as an enterprise data storage solution and make the data stale.
Companies should always work on their data strategies and these data strategy should bring in governed data democratisation and data literacy across the enterprise which can act as a catalyst for the overall business performance, empower users and bring in autonomy.
Organisations should not encourage their teams taking short cuts and just look for short-sighted success while working with the data (if their overall vision is for a long run). I would like to term this process of taking data short cut steps as 'data-jaywalking'. If companies want to perform any business intelligence, analytics or data science, leaders of the business units should ensure that data engineers, analysts and data scientists should not take any unscientific shortcuts. They should avoid blind spots in performing their tasks and in building AI models. Overall success should not be just measured by PoC or AI labs prototype results, instead it should be measured from at-scale deployment and impact made to the business and end users.
Data leaders (e.g., CIO, CDO, CAO and others) should always ask the right data questions, involve, supervise all stages of data curation, data prep stages and scrutiny while establishing a robust data corpus across the organisation. This will form the bed rock for analytics, data science/AI model builds and deploy while coupling with AI model governance.
Once the corpus of data is ready, organisations can get suitable tool(s) to build and gain insights. Going down the data analytics journey lane, organisations may face few hurdles while building and managing their set data strategy into action (e.g., challenges can be from architecture, data infrastructure, competencies, running costs, business user’s expectation, people etc.). These challenges have to be rectified and overcome at the earliest.
Companies have to consider data at its epitome for their growth, for being competitive, for being smarter, innovative and be prepared for any unforeseen market surprises.
Hope this article content makes sense and resonates with any of your own experiences and thoughts? Feel free to share your ideas, feedback and comments.
Bio: Pradeep Adaviswamy is Regional Manager of Analytics at Bahwan CyberTek. He has around 17 years of experience in Solution Design & Architecture, Delivery Management of; Big data, BI, Data Science & Analytics, Enterprise Data Lake, Data Management & Data Governance Practitioner (DMBoK), ETL, Data Warehousing, Data Modelling, Data Visualization and Artificial Intelligence.
Original. Reposted with permission.
- What every Data Scientist needs to learn from Business Leaders
- 3 Key Data Science Questions to Ask Your Big Data
- 5 Big Trends in Data Analytics