FAQs for Data Science

Simply put, data science means applying predictive analytics to get the most value out of your organization’s information. It’s not a product, but a group of interdisciplinary tools and techniques — integrating statistics, computer science and advanced technology — that help you turn data into strategic insights.

Most companies are overwhelmed with data today and are probably not leveraging it to its fullest potential. That’s where Hitachi Vantara comes in, offering unique data science capabilities that help you translate information into meaningful strategic insights — and a real competitive advantage.

By applying data science, your organization can confidently make decisions and take action, because you are working with facts and the scientific method, instead of intuition and guesswork.

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.

Data scientists are skilled in three disciplines: applied statistics and mathematics, computer science, and business and domain expertise. While data scientists may have backgrounds in physics, engineering, mathematics and other technical or scientific fields, they also need to understand your organization’s strategic goals so they can deliver real business benefits.

The everyday work of data scientists involves defining a business problem or opportunity, managing and analyzing all data relevant to the problem, building and testing models to provide insight and predictions, presenting results to business stakeholders, and then writing computer code to execute the chosen solution. In writing code, they are applying their mastery of a combination of languages used for data management and predictive analytics, such as Python, R, SAS and SQL/PostgreSQL. Finally, data scientists are also responsible for analyzing and reporting on the actual business results.

Because there are so many specific skill sets involved, qualified data scientists are difficult to identify and recruit, as well as expensive to maintain as part of your internal team. Most organizations choose to leverage the established, proven expertise of providers like Hitachi Vantara. Hitachi offers world-leading expertise in solving data-related challenges for customers in a wide range of industries in a flexible, cost-effective manner.

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.

Data science can deliver an enormous range of financial results and strategic benefits, depending on your organization, its specific challenges and its strategic objectives.

For example, a utility could optimize a smart grid to minimize energy consumption, by relying on real-time usage and cost patterns. A retailer could apply data science to point-of-purchase information to predict future purchases and custom-tailor product assortments. Automakers are actively using data science to gather real-world driving information and develop autonomous systems via machine learning. Industrial manufacturers use data science to minimize waste and maximize equipment uptime.

Generally speaking, data science and artificial intelligence are behind the advances in text analytics, image recognition and natural language processing that are driving innovations across all industries.

Data science can significantly improve performance in almost any area of your business, including:

• Optimizing the supply chain.
• Increasing employee retention.
• Understanding and meeting customer needs.
• Forecasting business metrics with precision.
• Tracking and improving product design and performance.

The question is not, what can data science do? A more accurate question is, what can’t it do? Your business already has huge volumes of stored information, as well as access to critical external data streams. Data science can leverage all that information to improve virtually every aspect of your performance, including your long-term financial results.

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.

Data science is becoming increasingly automated, and the pace of automation is sure to continue. For example, today a data scientist can set up a machine to do an automated grid search of all possible combinations of thousands of data parameters to find the best possible solution to a given problem in real time.

Historically, predictive models had to be designed and tuned by statisticians manually over a long period of time, using a combination of statistical experience and human creativity. But today, as data volumes and the complexity of business problems have grown, this type of task is so mathematically complex that it must be addressed via artificial intelligence, machine learning and automation. This trend will only continue as big data gets even bigger.

While AI and ML are often associated with the elimination of human workers, in fact they only increase the importance of data scientists and related fields. Achieving a competitive advantage when every company has access to these technologies will require continuing innovation and new approaches that test the current limits of statistics, computer science and domain expertise. It will be up to data scientists to provide new theories, new R&D, and new ad hoc applications of AI that enable the next generation of strategic and financial results.

There is no indication that automation will replace the need for skilled data scientists, data engineers and DataOps professionals, such as those at Hitachi, because so much human creativity is needed across a number of steps to capitalize on the full power of automation and AI.

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.