Data Management

Data Management

There is a mind-boggling amount of data floating around our society. Big data is an elusive concept that represents an amount of digital information, which is uncomfortable to store, transport, or analyze. Big data isn’t new. Fifty years ago, data was stored in a single mainframe computer that filled an entire building. To analyze the data, physicists from around the world traveled to connect to the enormous machine. In 1989, the Internet took off and physicists could then access the terabytes of big data remotely from around the world, generate results, and write papers in their home institutes. Then, they wanted to share their findings with all their colleagues. To make this information sharing easy, the web was created in the early 1990s, and physicists no longer needed to know where the information was stored in order to find it and access it on the web. This idea caught on across the world and has transformed the way we communicate in our daily lives. During the early 2000s, the continued growth of big data outstripped the capability to analyze it, despite having buildings full of computers. They started distributing the petabytes to collaborating partners in order to employ local computing and storage at hundreds of different institutes. 

Big data is so voluminous that it overwhelms the technologies of the day and challenges us to create the next generation of data storage tools and techniques. An alternative, more business-like approach for accessing on-demand resources has been flourishing recently, called cloud computing, which other communities are now exploiting to analyze their big data. It might seem paradoxical for a lab focused on the study of the unimaginably small building blocks of matter, to be the source of something as big as big data. As the old metaphor explains, “the whole is greater than the sum of its parts,” and this is no longer just science that is exploiting this. The fact that we can derive more knowledge by joining related information together, and spotting correlations, can inform and enrich numerous aspects of everyday life, either in real time, such as traffic or financial conditions, in short-term evolutions, such as medical or meteorological, or in predictive situations, such as business, crime, or disease trends.

We can have a lot of data and that causes information chaos. Organizations are struggling to manage the growing volume, velocity and variety of enterprise data. In today’s information-driven economy, data centric initiatives such as data governance and business intelligence are the forefront of many organizations’ strategic priorities. Traditionally, we would be talking about databases, but we are seeing lots of variety of data: image files, audio files, videos, and the speed at which this information is coming through as well. We are now sampling data down to the millisecond, from different sites, from different systems, and as we move into the future, from individual meters in homes as well. This explosion of information within the organization is preventing people from being able to find the right information at the right time. We know the data is out there somewhere, but how do we get the right bit within an acceptable time window? Once we have that data in this organization, what do we do with it? How long do we keep it for? Does regulation say we have to keep it for a certain amount of time? Can we let go of it at certain times, and if we can, why shouldn’t we? It is costing us in terms of storage and space. All those thing roll up into increasing costs, less confidence in the data that we hold, which is all adding risk to the organization.

Data runs our business, whether it is making strategic decisions based on quality information or making data management more efficient to save time and cost, data modelers play a critical role at the center of these initiatives, providing a collaborative data modeling environment that helps ensure that data is managed efficiently and with higher quality. With increasing demand for information from a variety of stakeholders, the need for collaboration is greater than ever before, but resources in most organizations are decreasing, with fewer staff to manage this ever-growing array of data sources. Companies have begun to understand that their data has the potential to provide a greater competitive advantage than traditional assets such as inventory, property, equipment and cash. However, without an understanding of where data has come from, how it was delivered and what it contains, the insight that this data can deliver is still limited. Through effective use and management, metadata will become the glue that holds together your organization’s information infrastructure. Metadata supports analytics, reporting, data quality prediction, dash ball integration, and business processes making it a key asset.

Objects that hold value are generally classified as assets. This could include information, software, reputation, people and services, in addition to IT equipment and infrastructure. Ideally, organizations would track every asset, but this generally proves to be too costly. And since they can’t track everything, the institutions usually decide what to track based on how important the asset is, by determining a number of factors like the sensitivity, criticality, value and compliance requirement of that asset. It is clear that business networks demand a robust enterprise management strategy. We start at the core of the enterprise, operational data, financial data, and plant material data. These are the lifeline and lifeblood of our core systems. We look at data as a strategic asset, and most importantly what sits behind that data is the metadata and we can use it to drive out hidden value. Data is pulled from many different sources and it comes into the analytical environment to rationalize key assets that can help explore an array of internal data, such as demographics, sales, revenues and customer behavior.

Traditionally, companies thought of their assets in two ways: one being the people that they’ve got as human capital, and that includes the skills, the experience and the expertise that they have within the organization. The other side of things is assets, where management is concerned with money, people, property, and inventory. But now, people realize that the data that they’ve got within the company is as important as the other two. Moreover, this information capital includes access to data, control of data and the ability to drive insight from that data. A well-designed information management governance collects, manages, analyzes and interprets data, while creating visual tools and technologies using industry tools and technologies. It helps us collect the right data to make the right decisions to respond faster to changes at less cost, leading to better business performance.

Data management is very important to business analytics. Recognizing the importance of information quality as a key business asset, organizations manage their complex enterprise data environment to allow disparate users to work together in an efficient way, saving time and money. A presentation model should be easy to understand and provide all the necessary data points for the business users to complete their analysis. The main focus is to formulate a strategy to outline the company’s key information assets, standards, quality assurance, ownership and governance of assets. “Master data management establishes the capabilities for defining the capabilities for defining and maintaining the company’s critical core data, and big data management captures very large amounts of business data from various sources and refine the data to be used in decision0making, to automate business processes and functions, to control some business assets automatically and provide automated or more adaptive services for the service consumes” (Arja Julin-Nurmi, 2013). Once data is managed, we can communicate this information to the diverse audiences and business units across the organizations in a format that users will understand in order to manage corporate data.

Data classification allows companies to apply appropriate safeguards, higher protection for sensitive data and wider access for general data. Leveraging data as a corporate asset drives greater efficiency and value. Once we have a better understanding of the type of data, we start to build up that confidence again. Moving from an information chaos perspective to information controlled perspective, we see the value of metadata, we reduce the cost, we reduce the risk, and we increase the trust we have in that data. Most importantly, there are a lot of models to understand how customers work and the sort of things they’re doing, and they come up with maturity models around these things to run a business. By understanding the data that we have within the organization, we realize the impact that it has allowing us to use that information as a strategic asset. We manage our data as strategic assets to achieve efficiency, gain insight, maintain flexibility, innovate and grow. Technology and data strategy and analytics, consumer behavior analytics, product marketing, data interchange, information integration, and information governance help companies to integrate and improve, consolidate and rationalize. Data across traditional and alternative asset allows for institutional investment screening, monitoring and peer comparison with advanced query capabilities to search traditional and alternative fields.

Virtually every field is turning to gathering big data, with mobile sensor networks spanning the globe, cameras on the ground and in the air, archives storing information published on the web, and loggers capturing the activities of Internet citizens the world over. The challenge is on to invent new tools and techniques to mine these vast stores, to inform decision making, to improve medical diagnosis, and otherwise to answer needs and desires of tomorrow’s society in ways that are unimagined today.