It wasn’t too long ago that data was gathered, analyzed, conclusions drawn from them and dashboards were instrument panels in autos and trucks. Today, and in the future, analytics is a must and dashboards are the narrow end of the data funnel where well organized data is management-ready in nano seconds

As a business strategy firm, CHEMARK over the years has promoted data accuracy as it’s hallmark. Believing the foundation of highly valuable strategic decisions must be grounded in accurate data first then to transmit this base accuracy into an effective strategy, that accurate data must be interpreted correctly.  Pretty simple . . . right?

We remain steadfastly loyal to that philosophy… Accurate Data provides the basis for accurate information which leverages accurate knowledge which provides an opportunity for strategic wisdom.

The process of assembling crucial data through primary research, and the gathering of accurate data requires the astute use of established market rapport techniques by associates that are expert in focused markets, combined with secondary research efforts to assure we know what is being broadcast through channels most anyone can access via electronic means.

Considered by itself… what is analytics?

Analytics is the discovery, interpretation, and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight.

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

SOURCE: Wikipedia

Analytics Segment Examples:
Marketing Optimization
Portfolio Analysis – Banks
Risk Analytics – Banks/Insurance
Digital Analytics – Business/Technical
Security Analytics – IT
Software Analytics

Data Density & Complexity & Need

It’s no secret that the amount of data and information that exists is growing rapidly with no sign of decelerating. Companies across industries are increasingly looking to capture new information, better use of current information and drive timely action based on analytics. However, the analysis is only as good as a company’s ability to capture the right data and manage it, which is where many companies are failing.

To produce quality and meaningful analysis companies are engaging with consulting firms to develop a strategy around how to capture, store and secure their data. To best meet client needs consulting firms are evolving their data management capabilities to address more specialized needs, including meeting the needs of inherited data structural design and new digital platforms and addressing regulatory needs around data quality, hosting and isolation.

In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of analyzing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly.

The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation. Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are rapidly becoming a relevant source of business intelligence for businesses, governments and universities. For example, in Britain the discovery that one company was illegally selling fraudulent doctor’s notes in order to assist people in defrauding employers and insurance companies, is an opportunity for insurance firms to increase the vigilance of their unstructured data analysis.

These challenges are the current inspiration for much of the innovation in modern analytics information systems, giving rise to relatively new machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentation.

Analytics is increasingly used in education, particularly at the district and government office levels. However, the complexity of student performance measures presents challenges when educators try to understand and use analytics to discern patterns in student capability ratings predict graduation likelihood, improve chances of student success, etc. To combat this, some analytics tools for educators adhere to an over-the-counter data format (embedding labels, supplemental documentation, and a help system, and making key package/display and content decisions) to improve educators’ understanding and use of the analytics being displayed.

Another emerging challenge is the dynamic regulatory needs. As can be appreciated in the banking industry, future capital adequacy needs are likely to make even smaller banks adopt internal risk models. In such cases, cloud computing and open source can help smaller banks to adopt risk analytics and support branch level monitoring by applying predictive analytics.

Analytics & Dashboards

As we view it, the pyramid illustration is the very foundation of analytics and the organization of the data gathered via analytics methodologies can be a significant management tool once this knowledge is placed into one of many possible dashboards.

So what is a Business analytics, how does it work and who benefits from the results? Business analytics refers to the skills, technologies, practices for continuous repetitive exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and steer business planning, which is also based on data and statistical methods.

Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, and online analytical processing .

In other words, querying, reporting, and OLAP can answer critical questions  . . . what happened, how many, how often, where the problem is, and what actions are needed. Business analytics can answer questions like why is this happening, what if these trends continue, what will happen next (that is, predict), what is the best that can happen (that is, optimize). 

Dan Adams, President of the AIM Institutre contributed content to this article.