Data is already pumping through organizations like never before. Recent studies show that the amount of data created over the next three years (to 2024) will be more than all the data created over the past 30. It’s not just growing in volume, it’s also getting more diverse. Customers are storing and analyzing data from all kinds of sources including machine data from industrial equipment, digital media, data from social networks, online transactions, financial analysis, and genomics research. In fact, the analogy ‘Data is the new oil’ is already considered outdated by many, who prefer to compare data with water – the most critical substance on earth, essential for the survival and nourishment of life. Organizational data is the most valuable asset for any business. However, harnessing the power of that data, discovering its untapped potential, and putting it to work to gain business advantage is easier said than done.
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Over 80% of data today is unstructured meaning it’s in the form of images, documents, sensor data, emails, geospatial data and more. According to Accenture’s report on ‘Closing the data value gap’, only 32% of organizations are able to realize tangible and measurable value from data. It’s survival of the most informed, and those that can put their data to work to make better, more informed decisions, respond faster to the unexpected, and uncover new opportunities –are those that will thrive.
Becoming a data driven organization
“Being a data-driven organization means culturally treating data as a strategic asset and then building capabilities to put that asset to use not just for big decisions but also for everyday action on the frontline.” —Ishit Vachhrajani, AWS Enterprise Strategist.
The main challenges that are holding organizations back from making better and faster decisions with data are silo-ed and slow data, poor data quality, lack of skills to use data effectively, poor enterprise wide strategy, and lack of leadership support for unlocking the value trapped in data.
The applications of data are vast and diverse. And harnessing this data to its full potential requires more than having just one data store or one data lake, it’s about having a complete, end-to-end data platform to store and access, analyze and visualize, and predict. And to ensure that the right people get access to data at the right time, you must also implement tools to manage security and access control for each of your data tools. To get here, you need to implement a modern end to end data strategy—one that can handle the enormous growth in data, one that can meet your various use cases for now and in the future, and one that can tie together the full data journey from storing data to putting that data in to action. With the right data strategy, organizations can control their growing data, find insights from diverse data types, and make it available to the right people and systems. There are three key elements to a modern end-to-end data strategy:
Modernize your data infrastructure
Organizations running legacy, on-prem data stores or self-managing in the cloud still have to undertake of a lot of management tasks. Running legacy data infrastructure on-premises or self-managed in the cloud is tedious, time-consuming, and expensive. You have to worry about hardware and software installation, configuration, performance and availability, scalability, and security and compliance issues. Building products based on the growing volume of data requires moving to a modern data infrastructure that lets you save time and costs while improving performance, availability, and scale.
Unify your data
Opportunities to transform the business with data exist all along the value chain. But making such a transformation requires that companies get a full picture and single source of truth of their customers and their business. This necessitates breaking down data silos and bringing petabytes of data together in one central place where the value of the data can be unlocked with easy, but governed data access to the people who need it, when they need it.
Innovating with data
And finally, you need to innovate with this data, creating new experiences and reimagining old processes with AI and machine learning (ML). Once the data is unified, you can then make this data available in a secure way to the people that need it through the right set of analytics, visualization, and ML tools for your unique use case to enable data-driven decision making. We are surrounded by machine learning innovation stories – from Bundesliga improving soccer fan experience with ML infused insights to Slack reinventing future of work with ML based tools and from Amazon.com tackling unforeseen supply demand forecasting challenges to Carbon Lighthouse fighting climate change with ML. Machine learning is one of the most disruptive technologies of our generation. It can help create entirely new revenue opportunities, make better and faster decisions, or improve operational efficiencies. In the fullness of time, virtually every application will be infused with ML and AI.
