Making a Modern Data Centric Organization — Part 2
How to make a Data Centric Organization? — Culture
Alright, we have discussed the What and the Who of Data Centric. Now, let’s talk about the how?
In my opinion, there should be four essential qualities to make a Data-Centric Organization.
1. CULTURE 2. STRATEGY 3. TECHNOLOGY 4. PROCESS.
Culture
1. A Data-Centric culture where everyone from Board room to Engine room till the shop room uses data effectively for Decision making and Data as an effective unit of Business outcome.
2. A self-serviceable culture
3. Have the right Mind-set, knowledge set, tool set, skill set, data set and make it all set to make a Data-Centric firm. To achieve this, you have your strategic workforce, upskill / reskill continuously.
4. Leadership should have Leaders from Data space. This should be non-negotiable.
Strategy
1. Most organizations think “Application-Centric” strategy rather than “Data-Centric” Strategy. Having the right organizational strategy that is well aligned with the Business outcomes supports a Data-centric culture.
2. From the business side if it is “Customer-Centric” organization, potentially the organization should have “Data-Centric” strategy that realizes the customer centricity. Because if you want to put the Customer in the center, you need Data.
Technology
1. Roadmap to Data Modernization that talks about the components of your Modern Data Centric organization
2. Modern Data Trends to Transform your organization
Execute and keep modernizing
Process
1. Shift in approach towards Data Management, Governance, Security, and mindset change.
Culture
Some more details on the how part, starting with CULTURE
1. Adopting a Data-centric culture starts from the Leadership team where any Decision that is made by the management team is always backed by strong data points even though human judgement is involved in making decisions.
This is the first essential step for the organization to walk in the Data mindset direction. The use of data does not only stop with the Decision-making process, for most of the organizations be it Legacy or Internet organization the input and the outcome is Data. Hence, from the producers to consumers of data, understanding that Data is the heart of the organization and maintaining the health of the data is imperative to the long-standing of Business and is a competition boost in order for your competitors not to eat your lunch.
2. once the Leadership is set, then eventually
All the Data Driven initiatives begin coming from the bottom of organization and the flow is set. Your team is modernized / empowered with off-the-job and on-the-job Trainings. Your core Data Team personas are defined across Simple users, Power Users and super users (could Enterprise Architects , CIO/CTO, Security, Legal, Data Analyst, Data Scientist, Data Warehouse users, Admin etc.,) …
The Data Leaders, CDO’s, Data Visionaries etc., are hired. Clarity in roles, responsibility, and accountability are defined
3. The Data teams can capture the required Data Sets and Information with ease. Analytics can be done with ease in the Data-Centric organization even if it is an after-thought.
Right self-servicing tools are available where Data is treated as a product or a Hub-and-spoke model or having a data market place whichever works efficiently.
4. You think about Data-first, when you start a new project / program and will potentially include it to the extensive Data Platform and will work on the application ecosystem to get value from Data. This is how you can build Data as a product or platform. otherwise Data-Silos is unnavoidable.
Strategy
The second element to the how part is the strategy and in my mind
1. Organization strategy
2. Data Stratey
Organization Strategy:
1. Any Data that comes from your existing customer / business, should be able to feed in to get new customer / business or at least help you sustain the current one. There are two schools of thoughts (1) Application Centric (2) Data Centric.
Applications come and go; Data is your permanent asset. All your applications are to source, process and output the DATA. Couple of decades ago we were using Mainframes, there after C/C++, after that Java / C#, now we are talking about R, Python, Julia etc.,? why? is Java outdated already — no, it is because the focus has shifted to Data centric… R and Python are more Data focused.
In an application centric organization, you will think Data in the aspect of application by application, what is the input needed, what it should process and what it should output. This way, you will never be able to think in terms of Data platform or Data product and you will continue to treat your Data in silos. Your focus will be to extract value from the given set of data, rather than thinking about synergizing data as a whole and treating it as a platform that provides value for the organization.
the second point i would like to focus on is the
Data Strategy:
We have to be mindful of what we call a Data-Centric organization:
With modern Data trends, we put a Data Warehouse or Data Lake or use Data Mesh or Data Fabric and call it a Data-Centric organization, no it is not. All we are trying to do is, put a massive dump of data into Data Lake and say, we bring all the data into a central place and now all the value is concentrated and there by we bring value to business.
Value of the data is not in gathering but processing the data
like the anology i used for crude oil to petrol.
A data strategy should help you
1. propel your organization growth with data value
2. lets you think in terms of platform, service, product rather than project by project or treating data in silos
3. data is considered as your permanent asset
4. Making Modern Data Organization from Day 1 rather than after thought.
5. extent of how “Data-Centric” you expect your organization in the next 2–3 years from now.
And finally your Data strategy is perfectly in sync with your organization strategy be it customer centric or otherwise.
Next: How to make a Data Centric Organization? — Strategy.
