Impact of Big Data in Enterprise Information Management

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Enterprise Information ManagementThe widespread impact of the upheaval caused by Big Data and the push for Advanced Analytics worldwide is well documented. One of the key components of business engagement is Enterprise Information Management (EIM) and as the white paper The Age of Analytics: Competing in a Data-driven World claims, comprehensive Information Management is only possible if the data sources are trustworthy, Data Quality is assured, data silos are interconnected, workflow management is smooth, and data access is provided only through role-based permissions. Big Data technology has the power to deliver all the above, while upholding good Data Governance strategies within the overall structure of effective EIM.

The Impact of Big Data across Industry Sectors
Data Scientists, now armed with superior computing power and vast amounts of omni-channel data, are in a position to deliver business insights at lightning speed. In spite of such advanced data technologies and tools at their disposal, enterprises are not yet fully prepared to handle Data Governance issues through mature process and policies. In many organizations, Data Analytics is pursued on ad-hoc basis: “structured role development in data analytics” is grossly lacking. The Big Impact from Big Data states that most enterprises now need to think in terms of data inclusion, bringing Data Science in the daily business workflow. As data-enabled decision making promises clarity and transparency, business executives at all levels should be inducted into the data-driven decision making paradigm.

Enterprise Information Management with Big Data

For years, data technologies have not meant much to mainstream business users beyond “technical gobbledygook.”  The emergence of Big Data promises democratization of Data Analytics and delivery of business benefits to the average business user. Big Data has opened up possibilities for speedy, economical, and more grassroots type of data solutions. However, given the mentioned benefits, businesses are not willing to bargain Data Quality or Governance, which they have come to expect since the days of Data Warehouses. Modern businesses cannot afford to get trapped into standard reporting by IT-savvy users, but need just-in-time, fast, and accurate information to aid daily decision making. The Oracle white paper titled Information Management and Big Data investigates a practical Data Architecture that can enable both Data Management and information access within a single framework to deliver solutions without costly data re-engineering or loss of service.

Take a look at these webinars for additional information: Data Lake vs. Data Warehouse and Leverage the IOT to Build a Smart Data Ecosystem.

Data Governance in Enterprise Information Management

Data Governance, unequivocally related to all core Data Management functions, is considered the single-most important component of EIM. For example, Master Data Management (MDM) cannot survive without effective Data Governance. The levels of controls, the governing styles and focuses may vary from one Data Management function to another, but the ultimate goal of Data Governance is to find the right level of control for a given function.

Data Governance comes with many challenges, which are most perceptible during implementation. Besides facing political and cultural resistances, the Enterprise Data Governance teams may confront many other roadblocks on the way to a successful Data Governance framework. This article stresses on the “people” factor of the process, which is far more critical than Data Quality and technology stewardship. People must be bought in to the Data Governance culture to ensure that fewer stumbling blocks such as “funding” surface on the journey. In other words, the influencers and the key executive decision makers should be aligned with the Data Governance team. Some steps to ensure the success of a Data Governance program are

  1. Develop cross-functional, Data Steward teams to facilitate efficient control of data assets.
  2. Define clear benefits like efficient task management, reduced cost, or increased revenue.
  3. Use metrics to promote the goals of Data Governance such as Data Management costs before and after or number of data-governed decisions.
  4. Provide incentives to early adopters to increase cross-function participation

Also see the article New Data Technologies Affecting Data Quality and Data Governance to understand how Big Data or IoT are affecting Data Governance issues.

Big Data in Enterprise Information Management: Sample Use Cases

TCS, the technology giant, clearly explains how business performance improves when Big Data technology is integrated with the existing Data Analytics platform in the brochure, TCS Analytics, Big Data, and Information Management Offerings. Once the broad benefits of Big Data Analytics have been reviewed, look at how specific industry sectors are utilizing this advanced data technology for Enterprise Information Management:

  • Big Data and EIM in Sales & Marketing:

In 10 Ways Big Data Is Revolutionizing Marketing and Sales, Forbes claims that customer analytics claims almost half of the Big Data pie, while the rest of the benefits are shared by operational analytics, compliance, new product and services, and Enterprise Data Warehouse optimization.  With Big Data enabled customer analytics, marketing and sales departments are well positioned to derive insights from omni-channel customer experiences.  Price optimization is possible today only because of Big Data algorithms and Advanced Analytics techniques.

  • Big Data and EIM in Pharmaceuticals:

In the pharmaceutical sector, data generation is continuously increasing from a cyclic network of R&D departments, product retailers, consumers, and healthcare providers. This vast network of data can help the pharmaceutical companies to identify new drug development opportunities and turn around approved products in record time. In the Big Data world, most data gets trapped electronically and instantly shared between business functions like the drug discovery (R&D) team, the clinical trials team, the partners, medical practitioners, and external research organizations.

The article, How Big Data Can Revolutionize Pharmaceutical R & D, discusses how eight technology strategies can help pharmaceutical companies to better manage and analyze the collected data through multiple interconnected sources. This Data Management approach can promote collaboration between all stakeholders while imposing strict control on which type of data is shared with whom. Forward thinking pharmaceutical companies are even developing “proprietary data repositories” to collect, prepare, analyze, and share critical data effecting new drug development process.

Big Data and EIM in Supply Chains:

Traditional supply chain systems were driven by statistics and measurable performance indicators. In How Big Data and Analytics Are Transforming Supply Chain Management, Forbes talks about the slow and gradual integration of Big Data technologies in supply chain Data Management. Now, Big Data controls everything from real-time analytics to sensor-based-demand supply control. As supply chain systems can be impacted by changing weather conditions or condition of machinery, Big Data technologies can aid effective decision making in times of emergencies. The Journal of Business Logistics published a paper investigating the probable use of Big Data Analytics within supply chain management, which proposes the use of advanced Machine Learning algorithms for forecasting restocking of goods. The goal of this paper is to enable warehouses and distribution centers to run without human intervention.

Big Data and EIM in Hotels & Hospitality:

Big Data can make valuable contributions to the hotel and hospitality industry as this industry typically deals with a wide variety and volume of customer data. The hotel sector welcomes millions of guests on a daily basis all around the world, and each hotel guest comes with a different set of expectations. The hotel guests begin their journey on the long data trail as soon as they book a room through electronic means, and continue till they check out. The hotel industry can use the valuable customer expectations into increased business opportunities through Advanced Data Analytics. As the article, How Big Data and Analytics Are Changing the Hotels and Hospitality Industry suggests, some hotel guests bring in more business than others by indulging in diverse types of activities and expenditures. Identifying those high-worth customers through behavioral analytics can result in huge benefits for hotel operators. Big Data Analytics can accurately differentiate between different types of hotel guests thus ensuring that all types of rooms from budget or economy rooms to luxury suites attract a steady stream of guests around the year.

KD Nugget’s Big Data Analytics in Hotel Industry explains how hoteliers can move beyond the “traditional loyalty programs” and offer more targeted services, depending on the behavioral patterns of segmented hotel guests, for example, business travellers, vacationers, sports-activity hunters, or casino lovers. The difference between tradition intuitive judgments and data-driven results is that the latter type is far quicker, more accurate, and more reliable than the former.

Big Data and EIM in Construction:

The global construction industry is in the midst of a radical, data-enabled transformation. Typically, large or medium size construction projects involve large databases requiring ongoing crunching during the project lifecycle. Moreover, construction firm owners need quick access to a wide variety of data like 2D and 3D models, accounting data, scheduling information, status reports, and other corporate documents.  The disparate data troves have to be linked for timely, data driven “modelling” or decision making. As a case in point, a new Big Data driven, building information modelling (BIM), system reduced the project cost of a civic center by $11 million, and cut short the project completion time by 12 weeks by shortening the modelling phase. If the construction industry stakeholders, the project managers, architects, engineers, contractors, and trades people learn to get out of their data silos and freely share information, then Big Data Analytics can promise a bright future for the construction industry. Find out more in How Big Data and Analytics Are Transforming the Construction Industry.
Building Successful Enterprise Information Management Systems

The DM Forum article titled Success in Enterprise Information Management for Big Data: Seven Points suggests some innovative methods for implementing successful EIM in enterprises. The underlying message of this article implies that continued efforts, an understanding of business requirements and structure, and effective project management are key to the success data-centric Enterprise Information Management.


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