By: Hu Yoshida, Vice President and Global Chief Technology Officer of Hitachi Vantara
2018 was a year of maturity for Digital Transformation, and most companies are committed to transforming their companies. They have laid out their strategies and are allocating resources to this transformation. Public Cloud, Agile Methodologies and DevOps, RESTful APIs, containers, analytics and machine learning are being adopted. Against this backdrop there are five trends for 2019 that I would like to call out.
Trend 1. Companies Will Shift from Data Generating to Data Powered Organisations
A 2017 Harvard Business Review article on Data Strategy noted,
“ Cross-industry studies show that on average, less than half of an organisation’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analysed or used at all.”
Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. In order to utilize the wealth of data that they already have, companies will be looking for solutions that will give comprehensive access to data from many sources. Data curation will be a focus to understand the meaning of the data as well as the technologies that are applied to the data so that data engineers can move and transform the essential data that data consumers need to power the organization. More focus will be on the operational aspects of data rather than the fundamentals of capturing, storing and protecting data. Metadata will be key, and companies will look to object-based storage systems to create a data fabric as a foundation for building large scale flow-based data systems.
Trend 2: AI and Machine Learning Unleash the Power of Data to Drive Business Decisions
AI and machine learning technologies can glean insights from unstructured data, connect the dots between disparate data points, and recognise and correlate patterns in data such as facial recognition. AI and machine learning are becoming widely adopted in home appliances, automobiles, plant automation, and smart cities. However, from a business perspective, AI and machine learning have been more difficult to implement as data sources are often disparate and fragmented and much of the information generated by businesses has little or no formal structure. While there is a wealth of knowledge that can be gleaned from business data to increase revenue, respond to emerging trends, improve operational efficiency and optimise marketing to create a competitive advantage, the requirement for manual data cleansing prior to analysis becomes a major roadblock. A 2016 Forbes article published a survey of data scientists which showed that most of their time, 80%, is spent on massaging rather than mining or modelling data.
Trend 3: Increasing Data Requirements Will Push Companies to The Edge with Data
Enterprise boundaries are extending to the edge – where both data and users reside, and multiple clouds converge. While the majority of the IoT products, services, and platforms are supported by cloud-computing platforms, the
Trend 4: Data Centers Become Automated
The role of the data centre has now changed from being an infrastructure provider to a provider of the right service at the right time and the right price. Workloads are becoming increasingly distributed, with applications running in public and private clouds as well as in traditional enterprise data centres. Applications are becoming more modular, leveraging containers and microservices as well as virtualization and bare metal. As more data is generated, there will be a corresponding growth in demand for storage space efficiency. Enterprises need to make the most of information technology—to engage with customers in real time, maximize return on IT investments and improve operational efficiency. Accomplishing this requires a deep understanding of what is happening in their data centres to predict and get ahead of trends, as well as the ability to automate action so staff are free to focus on strategic endeavours. A data centre is like an IoT microcosm, every device and software package have a sensor or log and is ripe for the application of artificial intelligence (AI), machine learning and automation to enable people to focus on the business and not on infrastructure.
Automation must be based on a shared/open API architecture that allows companies to simplify the transmission of data across a suite of management tools & 3rd party tools. Everything companies have must be API-based so that they can draw information in from other sources to create a more intelligent solution, and they can also pass information out if they’re not the master in the environment, so they can make other things smarter. There is a much broader opportunity to deliver better solutions if companies integrate with more vendors and partners.”
Trend 5: Corporate Data Responsibility Becomes a Priority
The implementation of GDPR in 2018 has focused attention on Data Privacy and required companies to make major investments on compliance. All international companies that are GDPR compliant now have a data protection officer (DPO) in an enterprise security leadership role. Data protection officers are responsible for overseeing a data protection strategy and implementation to ensure compliance with GDPR requirements.
The explosion of new technologies and business models are creating new challenges as companies are shifting from being data generating to data powered organisations. Big Data systems and analytics are becoming a centre of gravity as business realize the power of data to increase business growth and better understand their customers and markets. This has been fueled by the advances in technologies to gather data, integrate data sources, search, and analyze data to derive business value. The most powerful companies in the world are those who understand how to use the power of data. Relative new comers like Amazon, Baidu, Facebook, and Google have achieved their prominence through the power of data. However, with great power comes great responsibilities.
IT must provide the tools and processes to understand their data and ensure that the use of that data is done responsibly.
These trends represent my own thoughts and should not be considered representative of Hitachi or Hitachi Vantara.