The amount of data in both the private and public domain is experiencing exponential growth. One estimate asserts that 90% of all data in the world today has come into existence in the last two years and is expected to grow at a rate of 50% per year. According to an article in the October 2012 issue of Harvard Business Review, “we are now in a new regime: one where large amounts of relevant digital information exist on virtually any topic of interest to a business”. The volume of information is not only increasing in size, it is also increasing in its rate of creation and consumption. Mobile devices, sensors, audio and video feeds and other data sources and what has become known as “The Internet of Things” are all contributing to this increase in variety, volume and velocity of information expansion.
Managing large quantities of information could be viewed as nothing but a storage problem. However, when that information is interrogated using advanced analytic techniques, it presents an exceptional opportunity for disruptive business innovation. Advanced analytics techniques such as clustering, machine learning and sentiment analysis open new frontiers for information analysis – one in which past paradigms of business analysis shift from gathering “insight” into products, processes and organization, into one in which it is possible to gain “foresight.” These phenomena, collectively known as “Big Data,” have already enabled disruptive business models like Amazon, Facebook, Google, Netflix and Twitter to flourish and rise to prominence.
We are in the early stages of the widespread adoption of Big Data. For many organizations, knowing where to start, if at all, is the question. While there is a seemingly endless stream of information about the features and functions of Big Data technology, there is very little guidance on what to do with this technology. Questions such as, “What use cases would work for my organization?” “What information do I need and where will I get it?” and “How should I organize for success?” go largely unanswered.
Answering these questions requires more than an understanding of technology. It requires a deep knowledge of the business and a toolkit of techniques that can be harnessed to optimize each individual organization’s approach to Big Data.
Knowledgent provides the capabilities – people, processes and technology know-how – to leverage the Big Data opportunity. Our experienced Informationists, with our extensive expertise, tools and techniques, are uniquely qualified to get Big Data initiatives up and running.
Our Informationists understand business. This understanding enables them to frame opportunities for Big Data in the appropriate business context to engage business leaders, to select the appropriate use cases and to identify the appropriate data sources. Our Informationists are also skilled technicians, capable of exploring, modeling, analyzing and visualizing information using the latest Big Data tools and techniques.
We provide a range of Big Data services that can take an organization from ideation to operationalization.
- Big Data Innovation Workshops: Short duration, workshop-based approaches, focused on generating ideas within an organization. The goal is to build knowledge and awareness of Big Data capabilities, identify and rank potential Big Data use cases, perform proof-of-concept or proof-of-value level testing on high-potential use cases and prioritize successful use cases for further development.
- Big Data Architecture Design: Collaboratively develop a future state, technology component stack architecture for Big Data solution implementation. Develop architectural patterns that suit a category of Big Data solutions.
- Big Data Vendor Assessment: Assess technology vendors’ capabilities to provide the required Big Data component, features and functions in-line with each individual organization’s technology and budgetary constraints.
- Big Data Road Map: Develop a high-level, phased approach for the development and implementation of Big Data projects, processes and technology.
- Big Data Project Execution: Big Data projects require a unique approach and skillsets that differ from traditional development life cycles. Knowledgent brings the resources – people, methodologies and tools – required to execute a Big Data project through the prepare, explore, model, test and operationalize stages of development.
- Big Data Program Office: Implement a Program Management Office, with the appropriate managerial tools and techniques, to oversee the overall timeline (roadmap) and budget of multiple Big Data initiatives.
- Big Data Operating Models: Develop a “Big Data as a Service Model,” taking an overall organizational approach to operationalizing Big Data, including defining roles and responsibilities, workflows, policies and procedures and success criteria