DataOps started from a desire to deal with the data silo's, and enable non-tech savvy's to answer their questions with data. It is a best practice for handling data, making it most useful and valuable for companies. Continue reading to discover the most frequent data challenges companies are dealing with today.
The data challenges companies are facing today.
Big data is very complex, making it inaccessible.
The last years, there has been an enormous increase in the amount of collected data by companies. Next to text, images and video's, they now also store data of more complex sources like sensors, beacons, blockchain, IoT devices, etc. Also called big data.
This big data is not so easily accessible for everyone in the company as it should be. To start, you need an in-depth technical knowledge for this and access to all systems collecting data. Nor can you ask the IT team to do this for you, as they are already overloaded, and have no time to handle the mass of data requests coming in.
You could try to educate yourself data science and engineering skills; still, it will take you at least a year. And still, you would need to get the necessary access rights to get started.
No data strategy and governance in place.
Inspired by companies like Amazon and Netflix, companies wants to use big data to make better decisions. However, they are not aware what data is available, which information is needed to work more efficiently and support the business objective,s or how to implement all of this.
Often, a one-fits-all-solution is chosen and implemented by consultants as 'the' solution; pushing people to switch to a unified system (mostly ERP), and change their way of working tremendously. Not the best choice, as this leads to resistance to change, plus the data requirements of each business are different.
Even within a company each team has a different understanding of being data-driven as well as different data needs. For example: marketing needs the data for hyper-personalised and customer-centric campaigns, sales to not miss sales opportunities, product management to build the develop the right products for the market, etc.
For a sustainable switch to a data-driven culture within the company, you'll need in the first place a data strategy and governance that aligns everyone and gets the full support of all teams and especially the leadership team.
Using incorrect data, leading to wrong decision or business damage.
You can only make the right decisions if based on the right data. When you collect data from several sources without no proper validation, it will turn your data lake or database soon into a data swamp. Meaning, no one can still guarantee the correctness of the data for decisionsUsing incorrect data leads to a lot of frictions: customers getting wrong advice, receiving information about someone else, building the wrong product features, etc. Not only do you have unsatisfied customers, but you also put a lot of budget down the drain that could have been spend more efficiently.
Inefficient data profiles and not scalable data processes.
Many companies are looking into business intelligence, data science, and machine learning to improve their business. Due to the data disparity and complexity of big data, this is an arduous task. The in-house IT team has no overview of all data going in and out, how they are connected, nor what impact data operations will have on all components. There are also no scalable processes in places, so they are not able to cope with the ever-growing list of data questions and the speed needed to act fast upon market needs.
Next to this, companies often do not find or hire the right data profiles. For example, they hire data scientists to do all the data engineering work, or business analysts to run the queries and write data science scripts, DevOps are expected to have full data knowledge to set up right data processes.
In short, companies do not have the right data profiles and processes in-house, demotivating the employees and slowing down the data-driven journey.
DataOps to the rescue.
DataOps or data operations helps companies to overcome all these data challenges. DataOps makes your big data and process more manageable. It brings structure and methodology, provides the right people with the right tools and technologies, puts together expertise from different roles within one data team, empowers you to dare to fail. In return, your data becomes more valuable, leading to revenue growth in the long term.
It is a new practice that helps organisations to become data-driven in an agile, sustainable and scalable way. It encompasses data integration, collection, distribution, governance and monitoring in a secure way. It also speeds up the process of getting the right data with the right people, instead of waiting weeks for the requested data from your technical team, you can answer your data questions yourself in minutes.
It breaks down the silos that exist between the technical and business teams within a company, making data available for everyone who needs it to achieve the business objectives and not just the happy few (IT, data engineers, data scientists, etc.). The focus is no longer on the technologies, but on the people that work with the data. It converts big data into useful data for everyone in the company, supporting them to reach their goals and the companies objectives more efficiently.
Thus, in the end, DataOps makes your data more helpful and accessible for the right people in the company, enabling them to make the right data-driven decisions at the right moment.
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