Service Overview | Enterprise Data Management

Service Overview | Enterprise Data Management

A “Big Data” Approach

with a very large amount of very well-structured data, the amount of data and size of the database management system has to be very big though, but the data as such, based only on the volume, cannot be interpreted as “Big Data” in the narrow sense as it can be processed and analyzed rather quickly and effi cient.

From this point of view the mentioned complexity in the defi nition often arises not only from the amount, size or growth of data, but from its struc-tures (structured vs. semi-structured vs. unstructured) or needed, mandatory combination and interrelationships between different data sources (large number of data sources which are related or correlated to each other or which have to be used in order to get a useful information for business needs).

Furthermore “Big Data” topics deal with the problem that occurs from the combi-nation of volume, velocity and variety of data and their ensuing data processing requirements.

Understanding the Hype

To fi nd a well-fi tting approach regarding “Big Data” we fi rst have to understand the current conditions of the relevant data and determine if we are really dealing with “Big Data” in sense of complexity and / or technical limita-tions. Pursuing this approach we get a clear understanding if it is “just” a large amount of data or really “Big Data” as mentioned in the introduction.

Moreover the obvious goal of initially dealing with “Big Data” should be to manage this complex or unstructured data in a manner that information is

Introduction

“Big Data” is one of the most used terms in discussions regarding data warehouse and business intelligence topics lately.

After reviewing a relatively large amount of defi nitions the most common one includes a perspective to focus on data that grows so fast or large that it develops a complexity which is hard to be handled by regular database manage-ment tools.

The emphasis on the term “big” is often solely set to the term “size” or “growth”, which can be rather misleading:

First of all, “big”, “size” and “growth” can be interpreted in very different ways depending on the preconditions and needs of each stakeholder, owner or consumer of data. If we are e.g. dealing

Unleash the Hidden Treasures of Your Data

Enterprise Data Management | Service Overview2

accessible and can be analyzed. Deriving from this goal a company should first start to identify and prioritize relevant data, its structure and interrelation-ships in order to guarantee a valid “Big Data” approach in terms of methods and projects.

Due to the technical progress more and refined data discovery methods have been introduced. This leads to an even more complex and complicated task – identifying and including new sources in the scope of Information Management, using obviously unstructured data as e.g. user comments, feedbacks and customers reviews. Simultaneously new methods to model this data in order to store and analyze will have to be intro-duced, developed and used.

After all, “Big Data” is the unique possibility to enhance the organiza-tional Information Management. The reason of the hype is definitely the new approach to get additional informa-tion and insights which can be used to validate the overall approaches of a organization or to identify areas for corrective measurements based on the findings – a new layer of information can be combined to the already existing business-driven or financial information in order to add value to the business decisions and to unleash profit potential.

Discovering the Value

The opportunities can be discovered in different areas all over the value added chain of organizations.

A well-chosen example with a high value impact could be enhanced customer centricity due to new information about the client satisfaction, additional mood analysis or marketing testimonials derived from certain new information sources.

Other possible and worthwhile aspects could be detailed retention analysis based on context-based data discovery, driven by “Big Data” approaches or for example the combination of overall car insurance contract data and blackbox data from insured vehicles, especially in claim context driven processes, to assess additional potentials for the insur-ance company. Besides that, such data and analysis could be very useful for enhanced fraud detection concepts.

Taking the Journey

To effectively deal with “Big Data”, first a thorough gap analysis (detailed review of all relevant internal and external data) has to be performed in order to establish the IT enablement for “Big Data” usage. In this process simultane-ously a possible usage of next genera-tion DB systems can be assessed.

After this procedure various techniques have the potential to ensure the up- coming tasks will be done in a proper manner. Especially agile methods have been proven to support the “Big Data” approach as the requirements might change upon revelation of new or changed point of views. Furthermore rapid prototyping as a special agile method can deliver easy to analyze pre-results in order to validate the current way of managing and approaching “Big Data”. This can also involve business experts who get an early glance on new information added to known business or financial data. Visualization techniques can support identification, exploration and analysis of “Big Data”.

Quelle: www.gartner.com/it-glossary/big-data/

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Besides those special agile tools and techniques there are high numbers of possible entry points regarding the subject area of “Big Data”. After accessing and gathering “Big Data”, there are a lot of possibilities to analyze the new information in a useful manner: classification, cluster analysis, crowd-sourcing, association rule learning are just few of potential analysis approaches.

Even more: through exploring and identifying the informational value and securing this newly found information as asset, all those discoveries can be added to the knowledge base of the enterprise, adding overall value to the organization.

Getting Technology Stack Ready

Processing extensive data volumes create the demand for a new paradigm in technology. Traditional database systems cannot cope with increasing requirements on performance, avail-ability and capacity.

IT market and vendors currently focus on emerging technologies as well as on well-known concepts, which have a better fit for the “Big Data” application landscape.

The idea of (purpose-built) appliances is not entirely new, but gets now more important in the light of recent acquisi-tions of major SW vendors to become “full-stack” providers. Appliances are specifically engineered SW- and HW-systems and feature some advan-tages in comparison to classic systems, like plug-and-play configuration or almost endless scalability.

Other tendencies show the introduc-tion of alternative database concepts, like NoSQL, Columnar-oriented DBMS, as well as GraphDBs. These concepts introduce new capabilities for informa-tion exploration.

Finally, storage capacity and data access performance drive the evolution of future storage systems and – accordingly – data access methodology. Distributed

file systems, like Apache Hadoop, are first attempts to manage peta-byte scale data volumes. Intelligent data manage-ment systems need to manage data replication, failure detection, and data migration during failure, recovery or system expansion. Most of this technolo-gies are already available.

BearingPoint Approach

Besides the overall BearingPoint approach to support the integration of “Big Data” aspects into the overall BI and IM strategy (using MIKE2.0 meth-odology) we offer a consolidated and iterative way to address “Big Data” possibilities and opportunities:

In a first initial step we identify and discover new data sources and poten-tials and demonstrate the value for the customer by creating a business case or a value-driven proof of concept.

Based on the findings we support the client to find the best useful technology for “Big Data”, integration, visualization and all other aspects surrounding this initiative.

The goal for BearingPoint is to support and drive the efforts to make the organization “fit for Big Data”, regarding all aspects as for example processes or infrastructure. Furthermore Bearing-Point offers a phased approach based on MIKE2.0 for a solid implementation and realization basis.

Related Sources

Get more information on next genera-tion predictions – Hypercube

hypercube.bearingpoint.com

Visit BearingPoint’s (open) Methodology for Information Development – MIKE2.0

mike2.openmethodology.org

EDM@BearingPoint

For more information on BearingPoint’s Enterprise Data Management solutions, please visit: www.bearingpoint.com

Quelle: mike2.openmethodology.org

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