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In today’s data-driven culture, organizations across industries are approaching decision making from a quantitative point of view based on stats and figures, and relying less on gut feelings when making decisions. As they rely on data, they have expanded their strategy beyond back office and corporate applications. We now have Data Warehouses complemented by Hadoop based Data Lakes as a way to store the tsunami of data rushing in from social media and IoT sensors. Unfortunately, the more distributed nature of data today makes it difficult to get a holistic view of the business.
Metadata is critical to connecting distributed data, and providing context for decision making. It’s the knowledge sharing that describes and gives us information about data, and tells us things like where the data is coming from, what the data means, and how it’s related to other data – enabling that holistic view. Considering the digital evolution of business and the importance of data to business outcomes, it’s not surprising to see this monumental change happening in the role of metadata.
It’s important to recognize that some of the most important aspects of metadata for business involves discovering the types of metadata within an organization, cataloguing it, and then putting a business context around the data. If you don’t understand what data you have, if it’s not easily accessible, and if it’s not understandable by business people, then organizations won’t know if the data is actually applicable to a decision-making process, and if they can use it in compliance with data protection regulations.
How to Manage Metadata
Data serves as a roadmap for business decisions, but without metadata, you can only see a two-dimensional (2D) view – meaning decisions are being made without knowing the full context of what the impact/outcome could be. When metadata is applied, Artificial Intelligence and Machine Learning can create a more holistic view to make more informed, contextual business decisions.
In order for businesses to be successful at this, they must know how to properly manage their data and metadata, and the opportunities the information presents. Some steps businesses can take to properly manage metadata include:
- Aligning your information strategy with your business strategy
- Discovering what applications are part of the business process
- Understanding how poor Data Quality impacts business outcomes
- Recognizing the role of Data Governance in your business
Ultimately, there is a shift that needs to happen for organizations to move beyond 2D views of their data, by making the changes required for Metadata Management and Data Governance.
When applying Metadata Management, business information comes to life in 3D. Picture that you are trying to look at business data to learn how you can improve your customer experience. You can’t just look at data about your customer. You have to look at the web of information related to your customer’s experience and journey – who they interact with in your business, what channels they engage on, how efficiently issues are resolved, how often they engage, etc. In an increasingly distributed world of data, this interconnection is critical to achieving contextual insights that are needed to drive better business outcomes. With metadata acting as the connective tissue between different pieces of information, you see not just the data point, but all the relationships between the data points, thus bringing your data to life.
AI and Machine Learning: Making Technology Work for Humans
Artificial Intelligence and Machine Learning play an important role in influencing business decisions. These technologies can take on the automation of data discovery and data cataloguing within organizations—which relieves much of the burden of manually sorting—ultimately saving time and eliminating human error. Machine Learning can take things a step further, looking at how to improve the Data Quality based on metadata findings, and suggest data policies and quality rules.
Another benefit of this technology for humans is that Artificial Intelligence ensures strong, quality data sets are being applied to help train the Machine Learning algorithms. For example, metadata can help inform a Data Scientist which data sets may be most applicable to use for training an algorithm to positively impact a business decision. If you train an algorithm with bad data, the decisions it will make will not be optimal.
More importantly, metadata combined with Machine Learning can have significant impact to business processes. In the recruiting process they can be combined to automatically identify the best candidates for a given job description and remove subjectivity in candidate selection and hiring. Metadata and Machine Learning can boost automatic matching of incoming payments to invoices, which speeds up the account receivables process and lowers days sales outstanding. They can also help companies analyze their brand exposure from corporate events and sponsorships and increase return on marketing investment by recognizing logos in images and videos, matching that to the location of the logo in each image or video, and calculated the amount of time the logo is displayed.
The Future of Metadata
With Metadata Management bringing data to life in 3D, it’s only fair to look at how it’s impacting trends like augmented and virtual reality. Augmented reality (AR) is the real-time use of information in the form of text, graphics, video and other virtual enhancements integrated with real-world objects. Virtual reality (VR) provides a computer-generated 3D environment that surrounds a user to that individual’s actions in a natural way, although usually through immersive head-mounted displays. Metadata is what connects all the different information together, essentially bringing the images to life right before our eyes.
According to Gartner, the landscape of immersive consumer and business content and applications like augmented and virtual reality will evolve dramatically through 2021, thus further enhancing the integration across multiple mobile, wearable, Internet of Things and sensor-rich environments. Essentially, rooms and spaces will become active with things, and their connection through the metadata mesh will appear and work in conjunction with immersive virtual worlds.
Imagine a warehouse that can not only recognize the presence of workers, but can also help them understand the state of its equipment, and visually point out parts that require replacements. Although the potential of AR and VR is impressive, there will be many challenges and roadblocks ahead, including identifying key target personas and exploring targeted scenarios.
In any business, conversations about metadata and Data Governance need to become conversations about business outcomes that focus on things like how data quality impacts demand generation, converting interest into sales, creating loyalty, and generating active customer advocacy. Metadata helps in understanding where data is located, how it can be applied, and how it is linked to business processes and strategic initiatives. This in turn helps companies prioritize Data Management investments and activities based on business value. Ultimately, a successful Chief Data Officer must understand the relationships between data, policies, terms, rules and processes. When this happens, everyone making business decisions throughout an organization can access and depend on quality data to improve outcomes and more efficiently meet business objectives.