Reinventing Business Intelligence and Big Data Analytics

Business intelligence and big data analytics, as we know them, have largely failed. Massive value can be attained when businesses use data to understand and optimize their operations, and to outsmart and outperform their competition. But, except for a few giant firms with nearly unlimited IT resources and a strong background in technology adoption, most organizations lack either the expertise or budgets to effectively leverage big data.
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Three fatal flaws plague most analytics offerings today:

1: They focus on the technology and tools, not on the business questions

Instead of starting with the business questions that technology is required to answer, the focus has been on data management technology. But achieving greater business performance with analytics isn’t about the data. It’s about answering the right questions, which shape the business strategy and lead to better business outcomes.

2: They have a long and costly time to value

It can take a year to build a data warehouse, and months more to assemble queries, dashboards, and scorecards. This slow manual process generates outdated insights, which invariably lead to change requests and new questions that can’t be readily answered.

3: They lack domain expertise

Analytics engines have been built for the wrong people — IT professionals, data scientists, mathematicians, and analysts — who are in extremely short supply. While these professionals are technical experts, they typically do not have the business experience, insight, or context to know what business questions to ask—or the ability to act on the answers.

Turning analytics upside down

To realize the immense promise and potential of analytics, a fundamentally different approach is needed. And that requires that analytics providers dedicate their focus to business users and their domain-specific questions.

1. Start with business leaders and their questions

Business analytics are about finding answers, discovering new insights, linking insights to decisions, and modeling the probabilities of the future — all with the aim to shape business strategy in such a way that it drives business performance. All of this starts with industry- and domain-specific questions that are directly tied to business performance.

2. Combine technology with business domain knowledge

The birth of applied business analytics combines domain expertise with technological utility. Decision makers should be able to walk through a process of discovery by iteratively asking business questions and attain contextual, domain-specific insights instantly.

3. Modernize the enterprise software business model

Instead of the traditional business model, which has organizations being continually hit by an avalanche of statements of works and development costs, the new approach to business analytics allows businesses to subscribe to a complete, pre-built solution — with all data management included — for a fixed and predictable fee, delivered from the cloud so that all innovation can be instantly shared by all users.

Outsmarting and outperforming with applied business analytics

This new approach to analytics allows organizations to get smarter. It gives them a better understanding of what is working and what isn’t. It shows them cause and effect, how variables are impacting business performance, and the probable outcomes if those variables are adjusted. It provides for real-time learning and instant adjustment, not needing to wait for the next planning cycle.

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Business Intelligence vs. Data Analytics

What’s the Difference?

There is a clear overlap that exists between Business Intelligence and Data Analytics, and this is evident by the fact that the two terms are used interchangeably with one another in seemingly every piece of literature which features one of the topics. Even though the vast majority of enterprise organizations deploy one right next to the other, the two processes are different, and one thing is for sure: you can’t successfully practice one without the other.

When asked, most industry experts will group the two terms together, but for those that are serious about turning data into actionable insights, it is important to differentiate the two. In an attempt to gain a clearer focus, let’s break each topic down.

Business Intelligence

Business Intelligence is the use of data to help make business decisions. BI, as it’s commonly referred to, is a broad umbrella term for the use of data in a predictive environment. Business Intelligence encompasses analytics, acting as the non-technical sister term used to define this process. BI often refers to the process that is undertaken by companies in order to learn from the data they collect, after they have analyzed it. Conversely, Business Intelligence can also be used to describe the tools, strategies and plans that are involved with data-driven decision making.

Data Analytics

Analytics is a data science. If Business Intelligence is the decision-making portion, then Data Analytics is the process of asking questions. Analytics tools are deployed when a company wants to try and forecast what will happen in the future, whereas Business Intelligence tools help to transform those forecasts and predictive models into common language. In today’s data-heavy marketplace, analytics solutions are used to provide descriptions of the ways a user can break data down and view the trends that occur over time. You set up a Business Intelligence initiative, but you do Data Analytics.

The Bottom Line

Data Analytics is how you get to Business Intelligence. The analytics process, however long it takes, is what brings business users to a place where they can accurately make predictions about what will happen in the future, and that’s exactly what makes a business “intelligent.” Data Analytics should be thought of as the question-answering phase leading up to the decision-making phase in the overall scheme of Business Intelligence.
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