E-Guide BIG AGENDAS FOR BIG DATA ANALYTICS PROGRAMS

Transcription

1 E-Guide BIG AGENDAS FOR BIG DATA ANALYTICS PROGRAMS

2 B ig data has become one of the most talked-about trends within the business intelligence (BI), analytics and data management markets. A growing number of organizations are looking to BI and analytics vendors to help them answer business questions in big data environments. These organizations require a strong implementation plan to make sure that the analytics process works for them. In this E-Guide, learn some of the challenges that need to be face with big data, as well as steps to analytics. PAGE 2 OF 13

3 BIG DATA ANALYTICS PROJECTS EASIER SAID THAN DONE BUT DOABLE Big data has become one of the most talked-about trends -- and yes, buzzwords -- within the business intelligence (BI), analytics and data management markets. A growing number of organizations are looking to BI and analytics vendors to help them answer business questions in big data environments. Unfortunately, gaining visibility into pools of big data is easier said than done. And with vendors marketing a wide variety of technology offerings aimed at addressing the challenges of big data analytics projects, businesses may be hard-pressed to identify the one that best meets their needs. So, what is big data -- really? A recent story by the IT publication eweek offered the following take on it, based partly on Gartner Inc. s definition of the term: Big data refers to the volume, variety and velocity of structured and unstructured data pouring through networks into processors and storage devices, along with the conversion of such data into business advice for enterprises. That hits the mark in terms of data management and the analytics part of the equation, but it misses the essential aspect of the business challenges PAGE 3 OF 13

4 surrounding big data: complexity. For instance, big data installations often involve information -- from social media networks, s, sensors, Web activity logs and other data sources -- that doesn t fit easily into traditional data warehouse systems. And in many cases, all of that disparate data needs to be pulled together in order to make sense of it on a broader level. That can have big implications for business rules, table joins and other components of big data analytics systems. The complexity of big data is what really makes it different from more conventional data when it comes to storage and query management, and it s the main reason why analytical database and data analytics software vendors have had to step up their game to help companies deal with big data. Understanding big data is the first step in assessing your technology needs and putting a big data analytics plan in place. The second is understanding the market and the current trends that are affecting organizations looking to derive business value, and competitive advantages, from increasingly large and diverse data sets. BIG AGENDAS FOR BIG DATA ANALYTICS PROJECTS Many businesses have always had large data sets, of course. But now, more PAGE 4 OF 13

5 and more companies are storing terabytes and terabytes of information, if not petabytes. In addition, they re looking to analyze key data multiple times daily or even in real time -- a change from traditional BI processes for examining historical data on a weekly or monthly basis. And they want to process more and more complex queries that involve a variety of different data sets. That might include transaction data from enterprise resource planning and customer relationship management systems, plus social media and geospatial data, internal documents and other forms of information. Increasingly, companies also want to give business users self-service BI capabilities and make it easier for them to understand analytical findings. All of that can play into a big data analytics strategy, and technology vendors are addressing those needs in different ways. Many database and data warehouse vendors are focusing on the ability to process large amounts of complex data in a timely fashion. Some are using columnar data stores in an effort to enable quicker query performance, or providing built-in query optimizers, or adding support for open source technologies such as Hadoop and MapReduce. In-memory analytics tools may help speed up the analysis process by reducing the need to transfer data from disk drives, while data virtualization software and other real-time data integration technologies can be used to assemble PAGE 5 OF 13

6 information from disparate data sources on the fly. Ready-made analytics applications are being tailored to vertical markets that routinely have to deal with big data -- for instance, the telecommunications, financial services and online gaming industries. Data visualization tools can simplify the process of presenting the results of big data analytics queries to corporate executives and business managers. Organizations that fit into the categories described above in relation to their data and analytics needs should begin by considering the following issues and questions, among others, before creating an implementation plan and finalizing their big data infrastructure choices: The required timeliness of data, as not all databases support real-time data availability. The interrelatedness of data and the complexity of the business rules that will be needed to link various data sources to get a broad view of corporate performance, sales opportunities, customer behavior, risk factors and other business metrics. PAGE 6 OF 13

7 The amount of historical data that needs to be included for analysis purposes. If one data source contains only two years of information but five are required, how will that be handled? Which technology vendors have experience with big data analytics in your industry, and what is their track record? Who is responsible for the various data entities within an organization, and how will those people be involved in the big data analytics initiative? Those considerations don t constitute in-depth requirements planning, but they can help businesses get started on the road to deploying a big data analytics system and identifying the technology that will best support it. PAGE 7 OF 13

8 FIVE FIRST STEPS TO CREATING AN EFFECTIVE BIG DATA ANALYTICS PROGRAM Organizations embarking on big data analytics s require a strong implementation plan to make sure that the analytics process works for them. Choosing the technology that will be used is only half the battle when preparing for a big data initiative. Once a company identifies the right database software and analytics tools and begins to put the technology infrastructure in place, it s ready to move to the next level and develop a real strategy for success. The importance of effective project management processes to creating a successful big data analytics also cannot be overstated. The following five tips offer advice on steps that businesses should take to help ensure a smooth deployment: Decide what data to include and what to leave out. By their very nature, big data analytics projects involve large data sets. But that doesn t mean all of a company s data sources, or all of the information within a relevant data source, will need to be analyzed. Organizations need to identify the strategic data that will lead to valuable analytical insights. For instance, what PAGE 8 OF 13

9 combination of information can help pinpoint key customer-retention factors? Or what data is required to uncover hidden patterns in stock market transactions? Focusing on a project s business goals in the planning stages can help an organization hone in on the exact analytics that are required, after which it can -- and should -- look at the data needed to meet those business goals. In some cases, that indeed will mean including everything. In others, though, it means using only a subset of the big data on hand. Build effective business rules and then work through the complexity they create. Coping with complexity is the key aspect of most big data analytics initiatives. In order to get the right analytical outputs, it s essential to include business-focused data owners in the process to make sure that all of the necessary business rules are identified in advance. Once the rules are documented, technical staffers can assess how much complexity they create and the work required to turn the data inputs into relevant and valuable findings. That leads into the next phase of the implementation, as discussed below. Translate business rules into relevant analytics in a collaborative fashion. Business rules are just the first step in developing effective big data analytics applications. Next, IT or analytics professionals need to create the analytical queries and algorithms required to generate the desired outputs. But PAGE 9 OF 13

10 that shouldn t be done in a vacuum. The better and more accurate that queries are in the first place, the less redevelopment will be required. Many projects require continual reiterations due to a lack of communication between the project team and business departments. Ongoing communication and collaboration leads to a much smoother analytics development process. Have a maintenance plan. In addition to the initial development work, a successful big data analytics initiative requires ongoing attention and updates. Regular query maintenance and keeping on top of changes in business requirements are important, but they represent only one aspect of managing an analytics. As data volumes continue to increase and business users become more familiar with the analytics process, more questions that they want answered will inevitably arise. The analytics team must be able to keep up with the additional requests in a timely fashion. Also, one of the requirements when evaluating big data analytics hardware and software options is assessing their ability to support iterative development processes in dynamic business environments. An analytics system will retain its value over time if it can adapt to changing requirements. Keep your users in mind -- all of them. With interest growing in selfservice business intelligence (BI) capabilities, it shouldn t be shocking that a PAGE 10 OF 13

11 focus on end users is a key factor in big data analytics s. Having a robust IT infrastructure that can handle large data sets and both structured and unstructured information is important, of course. But so is developing a system that is usable and easy to interact with, and doing so means taking the varying needs of users into account. Different types of people -- from senior executives to operational workers, business analysts and statisticians -- will be accessing big data analytics applications in one way or another, and their adoption of the tools will help ensure overall project success. That requires different levels of interactivity that match user expectations and the amount of experience they have with analytics tools -- for instance, building dashboards and data visualizations to present findings in an easy-to-understand way to business managers and workers who aren t inclined to run their own big data analytics queries. There s no one way to ensure big data analytics success. But following a set of frameworks and best practices, including the tips outlined above, can help organizations keep their big data initiatives on track. The technical details of a big data installation are quite intensive and need to be looked at and considered in an in-depth manner. That isn t enough, though: Both the technical aspects and the business factors need to be taken into account to make sure that organizations get the desired outcomes from their big data analytics investments. PAGE 11 OF 13

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