Failing to Succeed in Data Analytics? Try DataOps

We live in a Fourth Industrial Revolution, where data is the lifeblood of business. Those of us who harness the power of artificial intelligence, machine learning and augmented analytics to uncover insights from data are the ones who will be able to find better ways of driving efficiency, productivity and superior business outcomes.

According to a McKinsey report on advanced analytics in Asia, many companies are increasingly aware of the powerful impact analytics can have on the business, with almost half of them discussing the value of analytics in their annual reports and press interviews. Gartner predicts that by 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency. These are good business directions because our recent Qlik study with IDC shows companies with higher data-to-insights capabilities see a significant positive bottom-line impact as well.

Rethinking Your Data Analytics Approach To Drive Success

Building a thriving data-driven culture takes hard work. One of the significant challenges in achieving this has to do with the way we organise different data specialist groups for various tasks.

I find that the challenge with this structure is the lack of interaction between these data analytics teams. Overlaps in work and data silos can become familiar, often leading to inefficiencies, bottlenecks, and wasted time. Sometimes, different groups analyse the same data, but end up with entirely different results!

While this sounds like an absolute nightmare, what if I told you that there was a solution that can overcome all these challenges? Well, it exists, and it is called DataOps.

What Is DataOps? A Data-driven Approach

DataOps helps teams collaborate to deliver data efficiently. It’s not a product you buy, but a process you implement, just like the world-famous Kanban Principles from Toyota. Kanban improves manufacturing efficiency with measures that reduce excess inventory, untangle workflow bottlenecks, and eliminate duplicate work. It’s so effective that technology companies like Tesla, Apple, and Spotify put Kanban at the heart of their production methods to create better products we love.

DataOps combines a similar method with cutting-edge technologies such as real-time data integration and automation to overcome today’s data challenges. It requires businesses to streamline processes and train staff to use data analytics and data integration tools effectively. When implemented well, DataOps eliminates data silos and increases data accessibility to employees across the business, empowering teams to discover better insights faster.

Core to a DataOps framework's success is setting up an enterprise data catalogue – an internal marketplace that lists the data available for analytics. To drive success, organizations must automate as much of the enterprise data catalogue and data integration process as possible, so teams can dedicate more of their precious time and resources to deriving meaningful, actionable insights.

Shire, a global research pharmaceutical company, is an excellent example of a business that has deployed a practical DataOps approach. Shire’s data analytics teams successfully integrated data from numerous information pipelines into a single connected data source, helping employees extract valuable information efficiently and effectively. This approach empowers Shire’s teams at all levels with the knowledge to make trusted, data-driven decisions - from ensuring that manufacturing is efficient in improving the patient healthcare experience.

To achieve the real-time needs of DataOps, we need to move towards better use of data management technologies crucial to continuous data integration. While cloud data lakes and warehouses provide the foundation of this move, Change Data Capture (CDC) technology enables the real-time aspect of DataOps. CDC does this by deploying a non-invasive method that registers data changes from core application systems and databases and streams them in real-time to data management platforms.

CDC (which you can try here) works beautifully together with data lakes and warehouse automation to provide near real-time, analytics-ready data on-demand. This also lays the foundation for advanced applications, such as artificial intelligence and machine learning tools, to help teams uncover insights from data faster and more effectively.

Five Steps To DataOps Success

While implementing DataOps is no easy feat, getting started is much easier than it sounds. Here are five steps you can follow to prepare your enterprise for success with DataOps:

1. Think differently about data integration – With real-time analytics, teams cannot think of integration as batch processes that occur at specific times. You need to think in terms of continuous integration for data to meet today’s high-speed analytics demands.

2. Simplify your solutions – Drive more efficiency by reducing the moving parts for your data. Use solutions that support a wide variety of data sources, targets, and span multiple integration use cases. For example, simplify your data capturing process by capturing changed data only once per transaction system and route that data where and when needed: to a data lake; or a data warehouse; or replicate it to another database.

3. Take advantage of automation – Automation speeds up your DataOps initiative and accelerates everything – from the generation of change data streams to delivery, refinement, and the creation of analytics-ready data sets. This is crucial for data lakes, which receive a healthy flow of diverse data formats which are continuously changing. Automation is especially important in our ever-changing industry, where talent and skills are often in short supply.

4. Train up your agility with flexible data integration – Flexible data integration solutions enable your teams to change data sources or targets without disrupting the entire infrastructure – providing an agile, modern infrastructure ready for today and tomorrow’s data challenges. In a competitive, rapidly changing marketplace, the agility needed to embrace evolving customer needs quickly is vital to success.

5. Build data trust throughout the business – Ensure that your teams can make effective data-driven decisions by delivering them trusted data punctually. To do this, you need to provide users with complete knowledge of the data, from its origin to how it was manipulated. The key to tracking data changes is an architecture that enables a data catalogue to find information quickly; a data lineage to showcase the information’s origin and transformations, and validation to ensure that any data movements were successful.

With these five steps, you can establish a robust DataOps methodology throughout your organisation and enjoy the vast productivity gains brought by real-time, collaborative data analytics. Though we all face uncertain times ahead, DataOps is here to help your teams solve problems with data quality, trust, and access to analytics-ready data – empowering employees everywhere to make confident business decisions for success in today’s challenging business climate.

@Qlik's Geoff Thomas lays out 5 steps to prepare enterprises for success w/ #DataOps and to improve their #analytics competency & productivity.

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