FAQs for Data Science

The math and statistical theory underlying data science have been important for decades. But recent technological trends have enabled the industrial implementation of what was previously only theory. These trends are triggering a new level of demand for data science and an unprecedented level of excitement about what it can accomplish. They include:

• The rise of big data and the internet of things (IoT). The digital transformation of the business world has resulted in an enormous amount of data about customers, competitors, market trends and other key factors that affect your financial success. Because this data comes from many sources, and may be unstructured, it's challenging. It's difficult, if not impossible, for internal groups, such as traditional business analysts and IT teams working with legacy systems, to manage and apply on their own.
• The new accessibility of artificial intelligence (AI). Once science fiction concepts, today artificial intelligence and machine learning (ML) are commonplace, and just in time to solve the big data challenge. As data volume, variety and velocity have increased exponentially, the ability to detect patterns and make predictions is beyond the capability of human cognition and traditional statistical techniques. Today, AI and ML are required for robust data classification, analysis and prediction.
• Huge gains in computing power. Advanced data science would not be possible without recent enormous improvements in computer processing power. One critical development was the realization that computer processors designed for rendering images in gaming would also be well-suited for ML and AI applications. These advanced computer chips are capable of handling extremely sophisticated statistical and mathematical algorithms and delivering speedy results for even the most complex challenges — making them ideal for applications in data science.
• New data storage techniques, including cloud computing. Similarly, data science depends on an increased capability to store data of all types at a reasonable cost. Now companies can reasonably store petabytes (or millions of gigabytes) of data — whether internal or external, structured or unstructured — via a hybrid of on-premises and cloud storage.
• Systems integration. Data science brings together every part of your organization, so tight, high-velocity systems integration is essential. The technologies and systems designed to move data in real time must be seamlessly integrated with automated modeling capabilities that leverage machine learning algorithms to predict an outcome. Then the results must be communicated to customer-facing applications, with little to no latency, to seize an advantage.

The simple answer is this: You need to focus on data science because your competitors are already using it, and your customers expect it. Analysis-focused competitors are developing a deeper understanding of customers to improve sales, support and customer satisfaction. They are maximizing the efficiency of their processes for cost control. They are gaining insight into future trends for strategic planning. Perhaps most important, they are making decisions based on facts, not best guesses.

If you are not actively investing in data science, your organization will be outmatched and left behind in the era of artificial intelligence and the data renaissance.

Hitachi Vantara has established itself as a clear leader in data science, delivering strategic insights and supporting a fact-based decision-making process for a wide range of customers. With nearly 110 years of success in operational technologies and 60 years in IT, Hitachi has a unique understanding of how businesses work — and how data science can make them work better.

Whatever your unique strategic objectives, Hitachi's expert data scientists can gather and mine your existing information, incorporate third-party data streams as needed, apply the most advanced analytics, and recommend tactical action plans that move your organization forward. Data experts at Hitachi can help you predict outcomes and then compare the actual results, creating a culture of continuous learning and improvement.

Hitachi has applied the enormous power of data science to solve a diverse set of customer challenges, and our data science team can do the same for your organization.

An emerging concept, DataOps — or data operations — is enterprise-level data management for the artificial intelligence era. By implementing an overarching DataOps strategy, you can seamlessly connect your data consumers and creators, to rapidly find and use all the value in your data.

DataOps is not a product, service or solution. It's a methodology, a technological and cultural change aimed at improving your organization's use of data through better data quality, shorter cycle time and superior data management.

Obviously, data science is a key concept in data operations. While DataOps spans the entire cycle of gathering and applying information, data science is a critical component in applying math, statistics, artificial intelligence and machine learning to make sense of your data. Data science supports the end-to-end DataOps process by translating raw information into actionable insights that help you realize your top-level strategy.

With industry-leading expertise in both DataOps and data science, Hitachi Vantara is a natural partner not only for extracting value from your raw information, but also for instilling a data-driven culture and mindset, making data a focus for your business every day.