Post 1 – Data Management
Businesses have learned how powerful data, particular data on their customers, can be. As IoT is generating more and more of that data, business leaders are recognizing just how much of an asset data can be. They are recognizing how core to their business platform data is, but they’re also recognizing how challenging managing, accessing, and understanding that data is.
Overcoming this challenge isn’t easy. The traditional approach to application architecture has resulted in many siloed applications and outdated infrastructures. Moving away from that traditional approach towards a more service-oriented architecture in order to thoroughly utilize your data doesn’t just mean investing in updated technologies but it means ensuring your people have the skills necessary for data management.
As this transition may take a lot of time and planning, the organization will need to plan for how they will manage cloud vs on premise data storage. How will they manage their older applications utilizing their older data practices while their newer ones will likely utilize the new approach? How can they do so in a cost effective way that supports innovation? Obviously, accomplishing this will lead to great agility and efficiencies in the business.
Currently, most data management initiatives are in analytics and cost reduction, and operational efficiencies. Leading data management initiatives utilize a bimodal approach towards data management. They differentiate between operational and mission-critical uses of data (mode 1) and experimental uses of data (mode 2). A hybrid approach of cloud and on-premises have been the preferred deployment option for managing these two data types.
Over the upcoming years, data management strategies will track, explain and advise on strategies, organizational models, practices, technology trends, and vendor offerings. This will help data analytic leaders address the above challenges as the differentiation between mode 1 and mode 2 will likely grow stronger.
Data analytics will need to study the current and future trends, and the impact of those trends of data management. They will need to be prepared to find the right balance of using new approaches vs old ones and be able to explain the value of those data management approaches.
References:
Edjlali, R., Heudecker, N. (2017). Data Management Strategies Primer for 2017. Gartner.
Post 2 – MDM Governance
Establishing a governing body with the responsibility for overseeing the master data and metadata management (MDM) is the only way MDM will succeed and deliver the benefits of this enterprise data strategy. The challenge most organizations face when establishing a governance team is identifying where to start. Governance teams need to start by recognizing and embracing the operational functions critical to success. They need to start by establishing design-focused governance practices:
- The identification and prioritization of master data domains.
- The identification and reconciliation of the business (semantic) and technical definitions — and usage of — the master data domains, entities, and attributes in scope for the initial and subsequent MDM implementations.
- Design and implementation of the domain authority models for master data creation and maintenance.
- Identification, prioritization, and incorporation of the source and target data systems and attributes.
- Creation and maintenance of the master data metadata repository.
The MDM Governance oversight will ensure proper business priorities are reflected in the MDM roadmaps and plans. It also provides an opportunity for the business and IT operation areas to be properly educated on the current state of master data, the benefits of information governance and MDM, and the level of effort required for the future state of the MDM.
The below figure is a high-level process model for the oversight of the design, development, and maintenance of the conceptual and logical data models, the required business and technical data transformation rules, and maintenance and publication of a metadata repository.
Data Domains that will make up the master data landscape will need to be identified and prioritized in a transparent and objective manner. Governance teams will need to come to agreed-upon definitions and attributes of the domains. This is important because often one entity of an organization will define a Customer (for example) in a completely different way than another entity would.
Upon completing the identification and prioritization of the domains, and their attributes, the Governance teams need to incorporate the domain list into the master logical data model, which can then be absorbed into the MDM physical model. This information, including the conceptual, logical, and physical data models should be retained in a tool like Sharepoint, although commercially available MDM software can be purchased.
References:
O’Kane, B. (2016). Governance of the MDM Design Process. Gartner.
Post 3 – Big Data and the Nexus of Forces
With Big Data and the Nexus of Forces making a considerable impact on how organizations manage data, enterprise decision makers are beginning to recognize the importance of information management.
The Traditional approach towards information management has been to build data models that reflect relevant information of the business’s strategic goals. The newer big-data approach doesn’t start with the top-down approach of looking at the strategic goals of the organization first like the traditional approach does, but it focuses on a bottom-up approach by looking at the data first and wondering what it means. With the newer big-data approach the innovation of the company lies within the data and by exploring the data new business strategies and business opportunities begin to emerge. Neither method, traditional or newer big-data, will outdo the other. To be successful an organization will have to reconcile both approaches within the same strategic framework.
The Big Data hype is expected to continue, so much so that soon big data will be known simply as ‘data’ once data sources become commonplace, technologies mature, skills become more prevalent and organizations implement enterprise-class big data solutions. But Big Data isn’t the only source of innovation in information management, the Nexus of Forces presents just as many innovative outlets.
The Nexus of Forces is known by Gartner as the convergence of mobile, social, cloud, and information to create exciting new business opportunities. This presents a whole host of new opportunities and challenges. With all of this information, companies are beginning to realize just how much of an asset data is, to the point that they’re monetizing their information assets via bartering or selling.
But all this data could be dangerous for companies. Consumers expect that if a company has data on them they (the company) will secure it and not sell it to other companies without the consumers’ permission. Either having the data stolen in a company breach or sold without permission damages a companies reputation and trust with their consumers are lost. Establishing Information Governance teams to monitor who has access to what is a first step towards ensuring companies protect this tremendous asset known as data.
References:
Buytendijk, B., Laney, D. (2017). Information 2020: Beyond Big Data. Gartner.
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