Lower Latency With Data Management

Data has been identified as a key strategic resource by businesses, and hence, they are increasingly embracing datacentric business models. A high level of data availability is crucial for drawing actionable business insights on time. Deepak Visweswaraiah, Senior VP & MD, NetApp India talks about how the company is helping organisations to compete in the current data-driven world with the right data management strategy and its ongoing investment in the data-driven technologies

- Soma Tah

Could you tell us about the data management challenges that businesses face when they adopt digital technologies, aI, and IoT?

Data today, is the lifeblood of any organisation. Businesses, in order to achieve digital transformation, need to swiftly adapt and outperform their competitors. They also need to be sure where their data is, how to manage and analyse, and to get insights from it to build better business models and experiences for their customers in turn. However, the availability of data affects the business models for almost all companies.

The companies are investing large amounts of resources in developing data architectures to help them realise the promise of a digital transformation. For example, enterprises are eager to take advantage of AI technologies to introduce new services and enhance insights from the company data. However, as data science teams move past proof of concept and begin to operate with deep learning, many are experiencing issues with data management. They may struggle to deliver the necessary performance, and also find it challenging to move and copy data and to optimise storage for large and growing datasets.

Building a data-driven organisation is challenging, but with the right approach, organisations can manage data holistically and modernise their architecture to address these issues. NetApp is uniquely positioned to provide important blocks of infrastructure and data management to help our customers implement their vision. For example, NetApp is partnering with several automotive companies that are gathering data from a growing number of vehicles. This data is used to train the AI algorithms necessary for autonomous operation. Retailers are creating inference models based on data gathered from point-of-sale devices across hundreds of retail locations around the world.