If you’ve read our previous blogs in this series, you’re familiar with the meaning of DataOps and what its major practices are by now. However, why should you go through all this effort? What’s in it for you and your company? This article will tell you the three main reasons to keep in mind when you consider implementing DataOps in your company culture, strategy and action plan.
Why you should care about DataOps
First, let’s take a look at the issues that can indicate whether you need a DataOps solution. These are prevalent issues in companies that use big data in their analytics projects and are thus facing issues due to data complexity.
When facing data challenges like big data complexity, a lot of people report that they have data governance issues, that they don’t trust the data or the quality thereof, they are struggling with non-scalable data processes such as time-consuming data cleaning tasks, which make for a slow process from data to insights. If your data and insights aren’t returning on their investments, leaving you with little to base your decisions on or to help you reduce risk, then you might want to consider implementing DataOps.
What can DataOps accomplish when applied, you ask? Only by implementing the DataOps discipline, with the full support of everyone, will you get these three business outcomes:
- Create value by making sure innovation or transformation projects get to add value on time. Value taking many forms such as a lower churn rate, higher conversion rates, correct insights, or a faster time-to-market.
- Improve efficiency by having faster data-to-insights-to-business decision flows, saving money with lower operational costs, improving your team's productivity, and scaling the processes.
- Militating and managing risk: having faster insights and more accurate data enables you to better predict and interpret the risks you are facing, and leaves you with more options to reduce and manage them quickly.
Let’s look at these three outcomes in detail below.
Value creation: Why DataOps facilitates you to not only become data-driven, but also value-driven.
What do we mean by value-driven? You can only be value-driven when insights are measured by their return on investment. Data alone doesn’t help anyone, but insights create value.
How to determine value from data
The amount of value depends on two things; on the one hand, the monetary gains from finding a new opportunity in the market, improving work processes and thus reducing costs, or innovating on existing products and services.
On the other hand, the value is negatively affected by the invested resources: capital, time, training and hires.
The difference with being data-driven is that you focus on the value of your insights and look at how it’s impacted. If you want to be value-driven, and get the most out of your data, DataOps will help you get better insights out of your data while reducing the cost of those insights.
How to get from data-driven to value-driven
How? By starting at the beginning. Let’s take a look at what this beginning looks like.
The focus of DataOps Implementation is not ‘what data and processes do you have in place, but what you ideally need. This differs for any organisation, so it’s important to define what could be valuable for your decision-making process and how we can get there.
This process includes seeing what data you have versus what data you could use, what processes you have versus what processes you should implement, to what reports you currently have versus what reports would truly benefit you.
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Implementing DataOps is a continuous process, and next to defining what you need, the next step is to see how you can get there as efficiently as possible.
The bigger the data, the better, right? Yet, big data comes at a cost, and that cost is time (and storage). The more complex your data project becomes, the more likely it will take longer to get results, escalating from the usual days to weeks to go from data to insight.
There are many possible reasons for inefficiency. Some are more prevalent than others, such as the issues related to data accessibility, which we talked about in a previous blog.
Improve efficiency with tools
Another prevalent reason for inefficiency is not scaling your tools along with your projects. Not getting adequate tools for the job leads to hours of data preparation for every report due to the lack of investment in automation, while the amount and complexity of data kept growing regardless.
After all, adapting your tools to bigger projects is crucial to efficiency. When you go from gardening to a field of crops, you won’t be using the same trowel for everything, will you?
This delay costs precious time of the analytics team that could be spent on generating more insights to drive the business. Worst case scenario, your insights are losing value by being delayed and hindering action, such as getting your monthly forecast in the last week of said month.
Improve efficiency with data literacy
The same can be said with not scaling your team or the knowledge -or data literacy- present within your team. This creates dependency for everything data and technology on a small team of people, creating a bottleneck for your entire organisation. But data literacy should also be a skill favoured amongst business teams to bridge the gap between teams, set grounded expectations, communicate transparently and gain more value from data insights.
When implementing DataOps, it is crucial to ensure all stakeholders are presenting from the start. Being part of the implementation team, should not require a degree in data science, analysis or management.
This prevents the implementation is based on one team’s side of the process. The first potential problem with this is the other team’s side not having a great reception to the one-sided plan. Secondly, you could be facing major gaps in your strategy. Both problems deteriorate your plan’s support and would be setting you up for failure.
Reducing & managing risk
Are you concerned about the security of your data, pipelines, and reports? While it isn’t the main focus of DataOps, the framework has many benefits for reducing risk.
Applying DataOps practices help you by decreasing costs and improving team efficiency, thus increasing returns yielded on data projects. It can also reduce and manage risk at many of the steps in the process, from data to insight, as a direct and sometimes indirect effect of using the DataOps framework. Let’s go through the various ways it reduces and manages risk.
Manage your risk better with the right insights
Let’s start with an indirect effect from what we’ve previously talked about, having more valuable and faster insights. It helps you detect risks or other issues, not only faster, and it helps you understand where they’re coming from, what causes them, and how you can prevent further risks or issues.
When using data for risk reduction, not having the complete picture because of incomplete data will only create a bigger risk. This can be due to long waiting times or having data quality issues. By increasing the efficiency of the data-to-insights process, and increasing the quality of those insights, you’ll
Next up is its approach to data accessibility. A common misunderstanding is that DataOps makes data accessible for everyone, but that isn’t true. The actual goal is to make relevant data accessible to the appropriate people, with a people-centric approach to data accessibility and access management. Important in this approach is that this accessibility is flexible, agile even, so the data security officer can easily grant or restrict access to specific parts or all data.
Risk management and regulations
What’s also important for data accessibility, is to comply with regulations and policies, and handle sensitive info with care. This also means not locking up your data in a far-away server, where it might be secure, but you can’t update it.
DataOps helps you set up a flexible data access management, masking sensitive parts of the data you collect, such as anonymisation, to comply with GDPR. A flexible data access management also ensures that only the people with adequate clearances can see data that could contain private information and can quickly update the data to comply with new regulations.
Another aspect of the accessibility is its preference for cloud-based data architectures, which can be secured easily and require less local storage. This way, you don’t have to invest in the more expensive secure on-premise storage, but it also means fewer data stored on personal devices, which could pose a liability ending up in the wrong hands. Next to this, cloud-based data allow for advanced yet flexible data access management, which also helps manage and reduce risks, as discussed above.
DataOps helps your business save time and money and gain opportunities your competitors are not aware of. It makes your data management bulletproof. Without a DataOps approach, your data projects either don’t make it to production, or it takes you too long to complete the project. This adds no value or gains at all for you and the company.
Working the DataOps way and implementing the discipline in your culture will enable you to create value, improve efficiency and reduce risk. Resulting in better data, better processes, better insights and better business results, and in the end, adding more value to the company.
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