Why your business decisions still rely more on gut feel than data driven insights.

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1 Why your business decisions still rely more on gut feel than data driven insights. THERE ARE BIG PROMISES FROM BIG DATA, BUT FEW ARE CONNECTING INSIGHTS TO HIGH CONFIDENCE DECISION-MAKING

2 85% of Business Leaders Believe Big Data will Revolutionize Business the Way the Internet Did Just a few years ago in 2013 Gartner reported that Big Data was a solution in search of a problem. At the time VP and Gartner Fellow Debra Logan was quoted to say While businesses are keen to start mining their data stores for useful insights, and many are already experimenting with technologies like Hadoop, the biggest challenge is working out what question you are trying to answer. Even banks are not doing Big Data in a production sense, because most of what they ve got is pretty well organized and in mainframes, she said. Certainly they re investigating it, they re wondering what it means, they want to learn about that kind of technical architecture and the kinds of methods you use to program and do analytics on it, but it s still in the early phases. Fast forward to 2015 and there are statistics indicating that 90% of the world s data was created in the last two years alone. As a society, we are creating more data in one day than has been created since the beginning of time. And, 85% of business leaders believe that big data will revolutionize business operations the same way the Internet did. Some have gone so far as to predict extinction for companies that do not embrace the Big Data revolution for competitive advantage. As a society, we are creating more data in one day than has been created since the beginning of time.

3 If you are a business leader, you have most likely responded to or seen plenty of reports that indicate the top 3 priorities for big data to have significant impact on an organization s future advantage are: 1 Impacting customer relationships 2 Redefining product development 3 Changing the way businesses organize operations So, if we have all this data available to us and every big data company out there is promising revolutionary insights, why is there still an interpretation gap? Why aren t companies able to realize the benefits of big data and analytics to their fullest extent? The reality is that there are several barriers organizations have not yet broken through. We see the obstacles for realizing the promise of big data and analytics fall into the following categories: We have found that success comes only from addressing these issues head on. That s why we developed the Brillio Data Platform to empower our customers to focus on generating breakthrough insights without worrying about the complexities of finding the right skill sets or selecting the right technologies and tools. We believe real value comes from delivering cutting edge technology, deep subject matter expertise, taking an open approach to problem solving, and maintaining an innovation oriented mindset Black Box Approach to Technology Deployment and Engagement Models The Data Itself and Organizational Structure People and Resources These challenges result in one thing: the failure to bridge the gap between information and insights, and as a result, the inability to make high confidence decisions that allow a business to function better.

4 PROBLEM Black Box Approach to Technology To guide decision making, the current state of the art approach used by the many advanced analytics firms is to build predictive models that include data mining and statistical pattern recognition techniques. The premise of this approach is to look at historical data and use it to draw inferences about what is likely to happen in the future using techniques of varying levels of sophistication. The result generated usually takes the form of are either backward looking reports or highly complex models. While these outputs can provide a prediction, they often fail to recognize the underlying why of the particular phenomena, and therefore continue to perpetuate an interpretation gap. SOLUTION Open, FlexibleTechnology The better alternative is a more open and collaborative approach. One that goes beyond the traditional one dimensional data crunching of historical information. For example, bringing a machine learning platform into the mix can allow companies to answer more pressing questions. This approach generates better results by combining what can be learned from historical data, and combines it with an understanding of the underlying knowledge models present in your business and industry (process model, behavioral model, etc.) and how they should behave. Couple that with external sources of data (social, weather, etc.) to create an intuitive decision making system. Only when you remove the black box model from the equation can you see all that is possible and achieve a higher level of confidence to determine what s next.

5 A Holistic, Forward Thinking, and Action-Oriented Approach to Data and Processes PROBLEM The Data Itself and Organizational Structure Most advanced analytics companies start their work once the data has been provided to them by the businesses or by IT. However, most organizations have a challenge making the right data available in a timely manner for analytical work. Additionally, as is often the case, the data that is relied upon for critical business decisions is often messy, unreliable, and in siloes throughout the organization. We find that organizations that have the most success with big data analytics make significant changes to mindsets, business processes, and partner to bring together data from various sources and systems. Taking a platform approach is the best way to accomplish this. A platform approach allows for simultaneous analysis of massive streams of unstructured and structured big data from both private and public sources. Unlike other big data analytics companies out there, Brillio brings together an integrated big data analytics environment, the best available technologies, services and people required to develop data applications in one single place for generating new insights rapidly. The result: and end-to end platform that breaks down siloes and provides fuller picture of the problem. To further complicate things, data is always changing, driven for example by changes in customer behavior and preferences, new corporate structures such as acquisitions, new regulations or compliance requirements, or even new technology initiatives in various department. At times, initial findings can lead to new theories and ideas about problems at hand, and in turn new data streams need to be integrated and built into models. In addition, data is rarely house in a central, easy to access space. It is commonly housed and owned by various business units. The business units often work to address issues in their department, without looking at contributing factors outside their area. As a result bringing all of the data together and breaking down silos can be complicated. Companies are also accustomed to looking at data in the form of reports, dashboards and scorecards. While somewhat useful, being constrained to these formats and models limits impact as well. SOLUTION As part of an overall approach and commitment to generating ideas and innovation more quickly, it is important to have direct access to entrepreneurial approaches, knowledge, relationships (academia, startups, established partners), and tools that allow you to accelerate enterprise innovation. We actively invest in new capabilities that allow our customers to utilize emerging technology such as machine learning, deep learning, predictive and causal capabilities and artificial intelligence. This gives us the ability to take knowledge to the next level by leveraging elements such as causal factor analysis and progression mapping, and allowing business leaders to uncover linkages and interdependencies. With these capabilities, companies are able to ask more meaningful questions, tackle not just the known/known parading, but dig deeper to find higher level of value by exploring the unknown. In other words, taking a rapid prototyping approach to big data projects will result in longer-term success. Companies also struggle with what issue to tackle first, and the result is inaction. While prioritization is important, it can slow progress. In order to accelerate the use of data, we believe that organizations need to get going, even if it initially involves making a series of little bets and experiments. This will put the business on the pathway to success by understanding, for example, the customer requirements or needs the current market dynamic will generate. Our people play a key role in this process, using their knowledge of the industry, technology and past experience to help clients utilize information housed across cross-functional teams, and bringing it together to achieve greater efficiencies and business impact.

6 PROBLEM Deployments and Lack of Visibility Many companies in the big data and analytics space have a highly custom, or a one size fits all solution. The former dramatically increases cost and deployment time, while the latter often fails to take into account the variations in industries and individual organization needs. What s worse is that the most well known firms often use an engagement model that requires companies to turn data over to them so they can crunch, build models, and come back with an answer. With this model, you are left in the dark and not given insight as to how the answer was achieved, resulting in the black box. Rapid Deployment and a High Touch Engagement Model When it came to building out a platform, we chose the best of breed, open source solutions. Our technology is designed to allow for rapid deployment times. The platform is constructed to be pre-configurated and utilize pre-integrated technology stacks that employ decision science capabilities to shorten the time to data access reducing the cost of implementation. However, we left room for customization and adaptation, so each solution meets client specific needs. This allows us to flexible and nimble, bringing our customers maximum ROI with minimal time and capital expenditures. SOLUTION Throughout the process, we utilize a high-touch engagement model that is focuses on creating a learning environment and collaborative experience. By approaching each engagement as a partnership, our people work as a cohesive team with clients throughout implementation and execution. This approach allows us to bring a higher level of engagement throughout the entire process, clearly define goals, constantly monitor progress and in turn become a key part of the customer s ecosystem.

7 PROBLEM People and Resources For companies, tackling a big data initiative requires investing additional skills, time, and resources, all of which are critical success factors that can make or break a project. Ensuring that teams can address the entire spectrum of industry business processes and have the practical expertise to first, pose the right questions for the project, and second, are predisposed to see the clues is a must. Specifically, a top performing team will include: Data engineers: Build bridges between your data and business. Data analysts: Part analysts, part technologists that know how to extract insights from data so you can get the why and how with confidence. Data scientists: Statisticians with technology understanding that transform current analytical models into prediction engines by integrating machine learning and other futuristic capabilities. Domain experts: Team members that sit within the data experts to insert the necessary industry dynamics, market understanding, and business model expertise into identifying critical insights. Business process experts: Part strategic and part execution, they bring in the knowledge and understanding of how functions and departments within the organization do things today. The reality is that most companies don t have resources to bring all of these skillsets to bear on big data projects, and without this broad skillset, impact is limited. SOLUTION Technology and Subject Matter Expertise We understand that technology advances rely on the skills of data scientists, data analysts and data engineers. We also understand that business impact is limited without the inclusion of domain experts, business experts and strategic partners. Our subject matter experts come from a variety of industries and roles, and this not only allows them to understand the complexity of big data analytics, but also the unique challenges each customer faces. For example, our financial services and banking customers often seek to increase product penetration while managing risk, our consumer goods and retail clients may want to leverage digital and social technologies and more effectively engage in omnichannel marketing, our consumer tech client often seek to identifying the emerging trends and align products with demand, and our utilities customers may seek ways to optimize business operations in a heavily regulated environment. Our industry knowledge allows us to uniquely tailor, design and deliver solutions based on individual markets and corporate needs, helping our clients identify what other data sets to incorporate, find new ways to apply information, and the next way to craft better questions that lead to insightful answers. The result is rapid prototyping capabilities that empower business and IT professionals to focus on generating breakthrough insights without having to master the complexity of tools, technologies or access to people with the latest skill sets and expertise.

8 Bridge the Gap between Information, Insights and Action Being able to understand the intricacies of big data analytics, complex and ever changing IT environments, as well as how key challenges often differ by industry and by client, are critical differentiators that allows Brillio to drive transformation through big data and analytics. The Brillio Data Platform was designed not to be a place where data is processed, but a solution that acts as a business accelerator. By bringing together big data analytics, user experience design, data science and the right manpower, we help our clients break down silos in data and across an organization, unearth interconnected decisions and factors, uncovers the why and what next, and identify new disruptive insights that will have a major impact on business processes and performance. Our investment in people and platform has given us the ability to pick up where others leave off, and fill the gap between data and insights. With these elements in place, companies can act, react and execute in ways that increase efficiencies and productivity, reduce time to market and costs, increase ROI and performance on KPIs, and deliver unique customer experiences. Read more at SILICON VALLEY 5201 Great America Parkway #100, Santa Clara, CA visit on Facebook at BrillioGlobal on Twitter on LinkedIn at BrillioGlobal

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