Five Best Practices for Data Management Optimizing the Use of Data for Business Intelligence and Big Data

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1 Ventana Research: Five Best Practices for Data Management Five Best Practices for Data Management Optimizing the Use of Data for Business Intelligence and Big Data White Paper Sponsored by 1 Ventana Research 2013

2 Table of Contents New Potential in Big Data 3 Prepare for Greater Volumes of Data 5 Gain Context from the Variety of Data 6 Exploit the Velocity of Data 7 Use Data Quality To Increase Trust 8 Demonstrate the Value of Improvement 9 About Ventana Research 12 2 Ventana Research 2013

3 New Potential in Big Data Big data offers great promise for businesses, particularly in enabling them to use data more intelligently for business analysis. The increased volume of data being generated and stored by businesses today clearly has tremendous potential, but it also poses great challenges, particularly for the technical staff that must manage it. In order for the value of big data to be maximized, IT must ensure the consistency, quality and timeliness of the data being analyzed, regardless of its size or velocity. Organizations face another data challenge as well: They must figure out how to analyze emerging and newly available varieties of data, among them text, geospatial data, events and streams. It is, in short, a brave new world of data, a complex data environment that requires skilled resources to successfully manage. On the importance of big data there is little dispute. Our benchmark research on business technology innovation shows that big data is important or very important to most organizations (62%); this is even more the case (76%) for very large companies. And they are moving forward to use it: Almost one in four (23%) have used it for more than a year, 18 percent have begun using it in the last 12 months, and another 23 percent will do so in the next year. Organizations are managing big data using new technologies. About one in five organizations plan to use in-memory processing (22%), Hadoop (20%) and data appliances (19%) by the end of 2014 far outpacing, for example, the planned adoption of relational databases (10%). 3 Ventana Research 2013

4 Using big data technologies, organizations are seeing positive results. More than one-quarter (28%) of the participants in our research said it has improved their activities and processes significantly. There are, of course, challenges, among them the time it takes to process large volumes of data, a situation further complicated by the rapid rate at which more data is arriving. Another is that some new technologies, such as Hadoop using MapReduce, require custom coding and specialized skills; the latter is in short supply, our research shows. To address this problem some vendors have developed standard tools that dynamically handle customization. Similarly data tasks such as integration and quality now can be accomplished through tools that have been proven in the past and adapted to support big data technologies. However, using these data management technologies typically necessitates the involvement of IT staff. In our view, IT s role is to provide leadership in a collaborative fashion, both guiding and educating business people on how to manage surging volumes of data while identifying investments in tools that can help. This guidance will enable business people to apply analytics to produce metrics that enable them to take action or make decisions. Investments in this area can pay off as big data adds business value, according to our research: More than one-quarter (28%) of organizations said big data has improved their activities and processes significantly. Using big data can also help improve communication and knowledge sharing (reported by 50% of organizations), management and tracking of initiatives (40%) and organizational alignment (33%). All of these benefits contribute to better business results and increased operational efficiency. 4 Ventana Research 2013

5 Organizations contemplating improvements in data management technologies need not reinvent the wheel. The application of best practices our research has identified as being used by the most mature and innovative organizations can enable sound decisions about strategy, approach and deployment in a timely manner. The following five best practices, derived from our benchmark research, can help an organization get a handle on today s data challenges and take advantage of the increased understanding big data and advanced business intelligence can provide. Prepare for Greater Volumes of Data Many organizations face challenges in the processing of data, our research reveals. Only one in five said they are very confident that they can manage the overall volume of big data they will need. As well, a large portion (44%) said their analytics processes are burdened by data-related tasks that prevent them from focusing on the analysis itself. To manage the rising tide of data it is necessary to increase the efficiency of these related processes, To gain the most complete view of the relevant aspect of a business, organizations must build that view using as many required sources of data, both internal and external, as are available. primarily by streamlining the manipulation of data into the right formats and its integration across the enterprise. Integration of data in particular can be a challenge. To gain the most complete view of the relevant aspect of a business, organizations must build that view using as many required sources of data, both internal and external, as are available. Without a controllable data integration process, however, this can get out of hand. Nearly one-fifth (18%) of organizations have to manage data from more than 20 sources, and one in nine (11%) have as many as 50 sources. It is not easy to manage the integration of this many data sources efficiently. Designed as they are to handle loading and storage tasks, modern data management technologies can deal with large volumes of data. Hadoop technologies are playing an increasing role: Already 30 5 Ventana Research 2013

6 percent of organizations use it, and in two years half will, our research shows. In preparing for greater volumes of data, a best practice is to have the IT organization examine the efficiency of its integration processes as they involve technologies like Hadoop and its ability to handle the increasing volumes that must be processed and stored. Gain Context from the Variety of Data Information is data placed in context; the more context there is, the more actionable and useful the information. Big data tools can enable organizations to maximize the value of their information in this regard. Incorporating an array of data sources and data types other than the familiar structured forms can yield far richer insights. Among sources, for example, market information, currently analyzed by 38 percent of organizations, is becoming a key category of big data. The greater context is competitively useful in examining data about customers, the category of data our research shows is the most widely analyzed (by 71% of research participants). Context can provide a competitive edge. IT organizations need to plan to integrate, store and use all available information to maximize its business potential. When it comes to types, data in text form, for one, can provide important context. Reports, documents and other written content are increasingly being used as a data source; three in five organizations (59%) said they now analyze content from documents and Web pages. Managing large volumes of nonrelational data thus is increasingly becoming a challenge for IT and business alike. Data today has a geographic aspect that is growing in importance. Our research shows that location information is very important to 41 percent of organizations, which are using it to inform their processes and improve their performance. Two-thirds (68%) said business employees use location data within the organization. But integrating and using geographic 6 Ventana Research 2013

7 information, which often includes images to establish the location context, can be difficult. Difficult though it may be to manage, context can provide a competitive edge. So IT organizations need to plan to integrate, store and use all available information, in its various forms, to be able to maximize its business potential. The best practice to gain better context in data is for IT to ensure it has these skills skills that may be new or unfamiliar and so may have to be sought out to be learned as well as a clear understanding of how the variety of information can be fully exploited by the business. Exploit the Velocity of Data Another data management challenge is velocity the rate at which data flows and must be processed for use. Like volume, the velocity of data continues to increase. Yet currently only one in six (16%) organizations are very confident that they can manage the velocity of big data they will need. Supporting faster processing of data is a requirement of every big data strategy; however, organizations must carefully evaluate technology designed to handle data velocity to ensure it can scale robustly enough to meet their needs both today and in the future. In-memory computing can speed the processing of data and help organizations deal with velocity. A new generation of technologies such as SAP HANA can increase timeliness by enabling analyses and decisions based on fresher data that is available real time and in-memory. Although they are not widely understood in business, in-memory databases are currently in use by 39 percent of organizations. Handling velocity effectively means an organization is able to maintain, and where needed improve, the timeliness of its processing of data 7 Ventana Research 2013

8 regardless of the speed at which it arrives. This capability is imperative in an array of business situations that require immediate use of data, such as for real-time interactions with customers or locating products anywhere in the supply chain. Likewise, processing of machine data (done by 45% of organizations) and data from systems and networks (44%) benefits from efficient high-speed Keeping up with its velocity is a key part of exploiting big data fully in any deployment. Ensure that the technology being used can process the full range of data. processing that will enable IT to optimize its resources while ensuring timely support of business. Another aspect of the big data universe is the support for events and streams of data to trigger alerts and notifications and thus increase organizational agility in responding to changing conditions and situations. But despite the importance of this issue and the availability of tools, currently only 16 percent of research participants are very satisfied with the timeliness of their eventrelated data. Keeping up with its velocity is a key part of exploiting big data fully in any deployment. The best practice in exploiting the velocity of data is to ensure that the technology being used can process the full range of data, from transactional and batch processing to real-time integration and use of events. Use Data Quality To Increase Trust It is notable that fewer than one-quarter (23%) of organizations are very confident in the quality of information being generated by their analytics. Clearly decisions based on inaccurate or outdated information are more likely to be bad ones, and our research shows that many organizations need help in this regard. Reviewing data for quality and consistency issues currently is the single most time-consuming aspect of the analytics process: 21 percent of organizations in our research reported that they spend most of their time doing that. Deploying data quality tools can help by identifying inconsistencies and discrepancies and eliminating the 8 Ventana Research 2013

9 tedious manual tasks that occupy analysts time, preventing them from making more valuable contributions providing information in an actionable context for decision-making. Almost two-thirds (64%) of organizations said that having multiple versions of the truth is a significant barrier to information management. Inconsistency of data can undercut quality. When the organization uses disparate systems that do not all contain the latest version of data, people may act on information that they believe to be the same as that being used by others use but that is in fact different and perhaps out of date. This issue can multiply along with the volume and velocity of big data and can significantly inhibit efficient use of data. To address the consistency challenges of large volumes of data, master data management methods applied to big data can increase confidence in the proper definitions and use of information. It is the responsibility of IT staff to ensure that the information on which the business relies is trustworthy and so decision-makers can act with confidence. The best practice in achieving data quality is to use tools that help IT profile and improve the reliability of data used for analytics. In addition, consistency in data can be established by using master data management tools, which ensure that consistent data definitions and structures are used across the business. Demonstrate the Value of Improvement Improvement is good, of course, but when improvement requires budget, its value typically must be demonstrated in detail and the return for an investment in it quantified for business purposes over measured periods of time. To identify the business processes that will benefit from investments in data management technology such as big data and business analytics, we recommend assessments and iterative 9 Ventana Research 2013

10 proofs of concept that examine and demonstrate value to ensure the right tools are selected. Our research shows that using big data technology effectively has helped 29 percent of organizations analyze more data; despite the youth of the technology, 13 percent also reported they have reduced the time required for data collection and preparation. These are the technology benefits our research found are most sought after in deployments; we advise evaluating how tools under consideration can provide these benefits. As part of efforts to maximize the value derived from data it is essential to clarify for all stakeholders the importance of information management and what is required to effectively support big data, business analytics and any other processes that rely on data. Our research shows that many organizations understand this: Threefourths asserted that improving their management of information is necessary (40%) or very necessary (35%) for effective business performance. Those that don t appreciate it risk being unable to keep up with those that do. Information is a key to managing a business well; good information, delivered in a timely way, can provide context that leads to the most effective actions. IT is well-positioned to work with the lines of business to demonstrate the value of big data and the tools to manage it properly. The need for this collaboration is illustrated by our research finding that only 14 percent of organizations are very satisfied with their big data technology. There is no easy path to managing big data effectively, but having the required competency and skills is essential. The best practice we recommend is to conduct cost/benefit assessments to determine where investments can best support critical business processes and ultimately profitability. Once started with a data management project, take an iterative approach, which can deliver business value both more quickly and more continuously than traditional (and longer) project cycles. 10 Ventana Research 2013

11 Information is a key to managing business well; good information, delivered in a timely way, can provide intelligence and context that lead to the most effective actions. For optimum success in data management, IT and business units must join forces willingly, as is the case in the most mature 19 percent of organizations in our research. Achieving that collaboration is well worth the effort. The most direct path to incorporating big data into an efficient and effective data management discipline is to identify and embrace the best practices being used by the organizations our research has found are the most mature in their information management. 11 Ventana Research 2013

12 About Ventana Research Ventana Research is the most authoritative and respected benchmark business technology research and advisory services firm. We provide insight and expert guidance on mainstream and disruptive technologies through a unique set of research-based offerings including benchmark research and technology evaluation assessments, education workshops and our research and advisory services, Ventana On-Demand. Our unparalleled understanding of the role of technology in optimizing business processes and performance and our best practices guidance are rooted in our rigorous research-based benchmarking of people, processes, information and technology across business and IT functions in every industry. This benchmark research plus our market coverage and in-depth knowledge of hundreds of technology providers means we can deliver education and expertise to our clients to increase the value they derive from technology investments while reducing time, cost and risk. Ventana Research provides the most comprehensive analyst and research coverage in the industry; business and IT professionals worldwide are members of our community and benefit from Ventana Research s insights, as do highly regarded media and association partners around the globe. Our views and analyses are distributed daily through blogs and social media channels including Twitter, Facebook, LinkedIn and Google+. To learn how Ventana Research advances the maturity of organizations use of information and technology through benchmark research, education and advisory services, visit 12 Ventana Research 2013

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