Kristina: Why will big data become an increasingly integral part of business?
Christophe Antoine, VP Global Solutions, Talend: Companies in every industry are completely dependent on their data in order to provide customer insights, identify new opportunities and create untapped revenue streams.
In big data architecture or collection, the essential piece isn’t the technology, the platform or the volume of the data, but rather the value you get out of data. This value is often hard to find, or determine and score.
The ability to score data value, thanks in part to general practice to determine the accuracy, pertinence and completeness of the data, is becoming more critical. Having data issues in your chain can cause improper decisions, missed opportunities to maximize business, and misinterpretations or misconceptions about your business.
Kristina: Explain how to work big data architecture.
Christophe: Big data is a big deal, and the race to harness the promise of data for more profit is on in almost every industry. Many business leaders wonder how to dive into the big data pool without drowning.
I think big data is a concept of science as opposed to a toolset or infrastructure, meaning it is less about the technology platform and more about what you can do with the data itself. Everything has to be tied together to get an accurate picture. Effective and modern data architectures are user-driven, built on shared data, automated, driven by AI, elastic, simple and secure. Establishing a modern data architecture with these characteristics will drive real business results.
Kristina: What are the common challenges to big data collection?
Christophe: One of the most common challenges associated with big data collection is the ability to bring all of the data points together and make sense of it, and ultimately providing that information securely and with governance.
It’s vital to have team members who are comfortable working with data management technologies. Data architects should be able to provide a deep understanding of what technologies to use, who needs to consume the data, and at what cadence data reports are needed. The reality is there is some data you will need now and other data you will need later. It’s often impossible to recover data, so employing predictive maintenance that allows your team to start from the ingestion point rather than going back in time will be invaluable. Organizations who invest in the development of their data upfront won’t face the challenges of having to go back and recapture data.
Kristina: Explain what data health is and its importance for digital businesses.
Christophe: Every business is now in the data business. Even before COVID hit, many organizations had already begun that journey. When we found ourselves face-to-face with a global pandemic, the need to transform became more urgent.
Data health is a comprehensive system that ensures enterprise data is ready to underpin all corporate action. Talend has identified four focus areas that establish data health, including reliability, visibility, understanding and value.
Data health goes beyond simple data management, taking into account both the fundamental necessity of data and the critical role of accurate and reliable data in corporate survival. It offers proactive treatments, quantifiable measures and preventive steps to identify and correct issues, ensuring that corporate data is clean, complete and uncompromised.
In the future, data health solutions will help create a universal set of metrics to evaluate the health of corporate data and establish it as an essential indicator of the overall strength of a business.
Kristina: What are your best practices for data collection?
Christophe: Start by asking who is going to consume the data on your platform, how frequently they are going to consume it and when they are going to consume it. It is also important to determine both the short-term and long-term goals and benefits of the data you are collecting, before you begin collecting it.
Once you have an established and specific process for your data scientists, refine and examine the data quality and data value. The overall health of your data will guide you in determining how much you need to invest into technology and infrastructure.
Data also needs to be traceable, so ensure you have data governance in place to manage data security and privacy.
Finally, automate as much as possible, but make sure there’s human analysis of the data to provide deeper insights down the road.
social experiment by Livio Acerbo #greengroundit #live http://www.bizreport.com/2021/05/expert-how-data-strategy-must-inform-decision-making.html