Business Intelligence

Executive summary

Business Intelligence

The origin and evolution of what is now called Business Intelligence are not as recent as the development of networking technologies and the computer itself. The origins are almost as old as the history of human civilization. Data has always needed to be collected for various purposes. Ancient civilizations required information about taxes, armies, population, and many more issues, requiring data to generate this information to be collected and stored. The first examples of written language is in fact data storage. The Roman Empire was exceptionally fond of bureaucracy of any kind and record-keeping, especially after the invention of better forms of paper. The challenge of storing more data and then retrieving it to generate information continues to the present day.

With the growth of electronic computing power, came the development of increased data and information storage and retrieval capability. Decks of punched cards, reels of paper tape, high speed and high density magnetic tapes, magnetic disk drives, and solid state storage units was the progression of technology to increase capacity and reduce space – several of the technologies are still being used today. Programmers had to develop increasingly complex Database Management Systems to manage the continuously growing stored data. The first revolution in data maintenance and retrieval was relational database technology where data was organized and stored in similar “business related” components – allowing a more organized and efficient way to access data according to known business rules and relationships. With the continuing advance of more robust networks, managing the everyday business transactions turned from batch processing to taking advantage of real time events – and with that an increasing demand on data capture and information generation quality. Unfortunately, the transactional data collection systems and tools of that era were inappropriate for the job of conducting business research and compiling usable information – as their primary purpose was to speed along the data capture process.

Business Intelligence began coming of age in the late 1980s as the potential value of the massive amounts of data began to be recognized as a business asset instead of expense. Business Intelligence began comprising a wide array of technologies, practices, and protocols required to produce quality and achievable business insights. The actual meaning of Business Intelligence may differ from company to company, depending on their business model and competitive position in the market. Nevertheless, even though there may not be a universal definition of Business Intelligence, the common theme meets four basic requirements – to produce timely, high-value, accurate, and actionable fact based insights to business challenges.

Business Intelligence is constantly evolving and increasing its importance to corporate and governmental entities. Business Intelligence has historically been associated with technologies of data warehousing, however recent technological advances are making business insight capable of finding and extracting data from fluid and unstructured source systems, such as social media, and then transform it into whatever will be needed for business analysis.

 

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Service Methodology

Business Intelligence today is an integrated hardware, software, and network solution designed to facilitate the efficient and effective use of data and information within an organization. Although Business Intelligence requirements can vary in different business sectors, and many tools are industry-specific, most Business Intelligence environments provide a similar core suite of capabilities. The data gathered for Business Intelligence usage originates in various “sources of truth”, from Enterprise based systems such as Customer Resource Management, Supply Chain and Demand Management, Product Lifecycle Management, and Financial Management – to spreadsheets, text files, machine logs, and social media – all translated and organized into Data Warehouses according to an organization’s specific needs and then presented in various ways understandable by business knowledge workers.

There are many reasons why Business Intelligence is an important investment made by organizations. When properly implemented, deployed, and maintained, Business Intelligence tools can bring many benefits to a company, not the least of which is to improve its performance. Business Intelligence allows more effective mapping and transformation of source data into beneficial information. With Business Intelligence environments, new and/or more specific views on organizational data can be quickly deployed. Data Mining can also be performed, which helps uncover relationships and patterns in data which had not been known before. Business Intelligence efficiently supports frequently changing management and operational processes. Without needing to change source data systems, correctly implemented Business Intelligence assists fact-based management and develops effective and consistent forward looking access to information on customer activities, current trends on the market, as well as supply chain performance.

In today’s fast changing, turbulent and unpredictable economy, where “extraordinary is ordinary”, having yesterday’s typical Business Intelligence system which provides only tools for reporting and analysis is not enough. Modern Business Intelligence platforms provide rich visualization dashboards, planning and budgeting solutions, scorecards, event monitoring, efficient in-memory processing engines for data analysis, advanced reporting features, mobile access, and workflow business rules components. Business Intelligence is more than just a technology platform, it is a process which works for any size organization to support data gathering and processing, data-based managing, and fact-based decision making for successful performance within an increasingly crowded marketplace. The Business Intelligence approach works so well because it integrates business process perspective, the customer perspective, and provides a way to quantify all the value chain drivers, not just the financial factors.

The majority of Business Intelligence implementations don’t deliver the anticipated results. In fact, Business Intelligence projects fail at an astonishingly high rate – between 70 percent and 80 percent, according to the Gartner Group. Organizations of all sizes suffer from countless oversights and poor judgment calls during planning, tool selection, and rollout – mistakes that can be detrimental to Business Intelligence success. Utilizing consulting services as provided by Mr. Kuzanek, who has experience with avoiding these missteps, will significantly reduce the chance of a failed Business Intelligence deployment.

 

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Service Options

Companies can elect whether they just require Appleton Greene for advice and support with the Bronze Client Service, for research and performance analysis with the Silver Client Service, for facilitating departmental workshops with the Gold Client Service, or for complete process planning, development, implementation, management and review, with the Platinum Client Service. Ultimately, there is a service to suit every situation and every budget and clients can elect to either upgrade or downgrade from one service to another as and when required, providing complete flexibility in order to ensure that the right level of support is available over a sustainable period of time, enabling the organization to compensate for any prescriptive or emergent changes relating to: Customer Service; E-business; Finance; Globalization; Human Resources; Information Technology; Legal; Management; Marketing; or Production.

 

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Service Mission

The Gartner Group recently conducted a CIO forum with a focus on what Business Intelligence and it’s follow on technology Predictive and Prescriptive Analytics looks like in the near future. The overwhelming results point to the benefits of fact-based decision-making across a broad range of disciplines, including: marketing, sales, supply chain management, manufacturing, engineering, risk management, finance, research, product development, and Human Resources. Major changes are also indicated to be imminent to the world of Business Intelligence and Analytics – including the dominance of data discovery techniques, wider use of real-time streaming event data, and the eventual acceleration in Business Intelligence and Analytics spending as Big Data continues to mature. Gartner also goes on to cite, as the cost of acquiring, storing and managing data continues to fall, organizations are finding it more and more practical to apply Business Intelligence and Analytics in a far wider range of business situations.

As enterprises continue to recognize the economic value of information, and see the opportunity to capture and apply ever greater volumes of detailed data, they will come to expect access to Analytics technologies capable of making sense from event streams. This goes beyond traditional and mainstream Business Intelligence to a breed of technologies capable of producing autonomous insights and inferences quickly.

Traditional vendors of Analytic platforms recognize that in order to expand their reach beyond traditional power users, they must deliver packaged domain expertise and applications to enable self-service by a wider range of users. Service providers are seeking to turn custom project work and domain expertise into repeatable solutions that can be adopted by other organizations more easily. The result is that end-user organizations selecting Analytic applications will have a significantly wider variety of possible providers to evaluate. Organizations evaluating software vendors will almost always find a service version of their packaged applications, and the similarity of product concepts will shift the emphasis of competition to the domain expertise embedded by the vendors into the application.

Despite the strong interest in Business Intelligence and Analytics, confusion around Big Data is inhibiting spending on Business Intelligence and Analytics platforms. Service providers will garner business by closing the gap between available Big Data technology and business cases. As Big Data matures and more packaged intellectual property is available, Big Data Analytics will become more relevant and mainstream.

Ironically, the confusion that surrounds the “Big Data” term and the uncertainty about the tangible benefits of Big Data are partially to blame for the soft Business Intelligence and Analytics market. In the interim, Business Intelligence and Analytics continue to remain at the forefront for CIOs, and service providers will attempt to bridge much of the confusion. The gap will close when Business Intelligence, Analytics, and Big Data become integrated within the same product offering. When the solution has found the problem, when the discussion has matured from technology to business, and when there will be more off-the-shelf capability available, Big Data Analytics will pervade almost all platforms of Business Intelligence.

 

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Service objectives

The following list represents the Key Service Objectives (KSO) for the Appleton Greene Business Intelligence service.

1. Data Integration
   To make sound decisions and comply with governmental reporting requirements, an organization must first establish a solid data foundation. This foundation must combine historical data with current values from operational systems in order to provide a single version of the truth that can be then used to identify trends and predict future outcomes. Data integration technology is the key to consolidating this data and delivering an information infrastructure that will meet strategic business intelligence (BI) initiatives and tactical and governmental reporting requirements. Data integration is the enabling technology for providing trustworthy information, enhancing IT and end-user productivity, and helping organizations achieve and maintain a competitive edge. Data integration enables mid-size and large organizations to effectively and efficiently leverage their data resources in order to satisfy their analysis and reporting requirements. While a home-grown data integration effort frequently yields a quick and dirty solution that may initially appear inexpensive, any upfront savings are often soon lost as demands on resources and personnel change. Vendor supported and consultant guided solutions, on the other hand, have withstood the test of time. Since they include capabilities such as metadata integration, ongoing updates and maintenance, access to a wider variety of data sources and types, and design and debugging options rarely offered by in-house solutions, they serve to increase the productivity of the IT organization. This is an important advantage as few organizations have unlimited resources and most are under constant pressure to do more with less. Additionally, most home-grown data integration solutions are rarely integrated with an organization’s BI tools. Such integration is, however, available with commercial offerings either by adherence to industry standards and/or through integration with the BI tools offered in the data integration vendor and consultant product portfolio.
2. Data Modeling
   Data Models visually represent the nature of data, business rules governing the data, and how it will be organized in a database. A data model is comprised of at least two parts, logical and physical. Data Models are created in either a Top Down or Bottom Up approach. In Top Down, data models are created by understanding and analyzing the business requirements, where in Bottom Up, data models are created from existing data structures – sometimes known as reverse-engineering. Data Models for transaction environments are created by normalizing the data into relationships, inheritance, composition, and aggregation properties. Data Models for warehouse environments seek to de-normalize data for performance. Data Models serve as the blueprint for building the physical database and applications which will rely on the data within the database. Improper or hap-hazard data modeling will only result in down-stream inefficiencies when developing or maintaining any applications. Data flow modeling is an approach which focuses on the flow of data between various Business Processes and helps to capture and document the information movements within an organization. In addition to the flow they describe data sources, destinations, storage, and transformations. Data models can be used effectively at both the enterprise level and on projects. Enterprise architects will often create one or more high-level logic models that depict the data structures supporting an enterprise, models typically referred to as enterprise data models or enterprise information models. An enterprise data model is one of several views that the organization may choose to maintain and support. Other views may explore network/hardware infrastructure, the organization structure itself, software infrastructure, and business processes.
3. KPI Dashboards
   Visual data discovery tools have grown in usage because of their agility. With production-style reporting, IT developers usually custom code a report based on specific requirements. With business query tools, IT first designs a data warehouse, and then models a semantic layer or business view based on subject areas in the data warehouse. Visual data discovery tools often allow users to bring their own data sources—whether modeled or not—into an exploration environment. Data may come from a data warehouse, and when possible, still have a semantic layer that may be optional and auto-generated as the data is loaded. In this way, if a company wants to bring in a new data source (whether big data from sensors or Web logs or small structured data from a supplier or distributor), the business user can do so with minimal IT intervention. Adding new data sources in a traditional data warehouse and BI environment may take months, in a visual data discovery environment, it happens sooner. Agility also comes from the ability to manipulate the data, dynamically creating bins, defining new groupings, or displaying values as percentages. More tools are providing lightweight data cleansing and transformations that business users can perform on their own. Dashboards, much like production-style reports, were once designed exclusively by IT based on predefined requirements. Each visual and interactivity, whether a drill or filter, had to be programmed in advance. Second-generation dashboards brought a greater degree of reusable components and out-of-the-box interactivity but were still largely developed by IT. Today, most visual data discovery tools support user-assembled dashboards and story boards. These visual data discovery tools are not solely for the data scientists and power users who want to explore new data sources, they also allow business users to assemble their own dashboards, bringing self-service BI to a broader class of users. Self-service BI doesn’t have to mean starting with a blank screen, it can mean navigating and exploring within a well-defined starting point. Dashboards provide this starting point by focusing all workers on the KPIs and business metrics that matter most.
4. Descriptive Analytics
   The goal of Data Analytics is to get actionable insights resulting in smarter decisions and better business outcomes. How organizations architect business technologies and design data analytics processes to get valuable and actionable insights varies widely. Nevertheless, it is critical for organizations to design and build a data warehouse / business intelligence (BI) architecture that provides a flexible, multi-faceted analytical environment, optimized for efficient absorption of and analysis on large and diverse datasets. Descriptive analytics looks at data and analyzes past events for insight as to how to approach the future. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Almost all management reporting such as sales, marketing, operations, and finance, uses this type of post-mortem analysis. Descriptive models quantify relationships in data in a way that is often used to classify customers or other business critical concepts into groups. Unlike predictive models that focus on predicting an outcome, such as customer behavior, descriptive models identify many different existing relationships between customers or products. Descriptive models do not rank-order customers by their likelihood of taking a particular action the way predictive models do. Descriptive models can be used, for example, to categorize customers by their product preferences and life stage. Descriptive modeling tools can be utilized to develop further models that can simulate large number of individualized agents and make predictions and then support business process simulation.
5. Predictive Analytics
   Predictive analytics has come of age as a core enterprise practice necessary to sustain competitive advantage. Predictive analytics turns data into valuable, actionable information. Predictive analytics uses data, usually from descriptive analytics, to determine the probable future outcome of an event or a likelihood of a situation occurring. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and gaming theory that analyze current and historical facts to make predictions about future events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The three basic cornerstones of predictive analytics are: Predictive Modeling, Decision Analysis, and Optimization Transaction Profiling. An example of using predictive analytics is optimizing customer relationship management systems. They can help enable an organization to analyze all customer data therefore exposing patterns that predict customer behavior. Another example is for an organization that offers multiple products, predictive analytics can help analyze customers’ spending, usage and other behavior, leading to efficient cross sales, or selling additional products to current customers. This directly leads to higher profitability per customer and stronger customer relationships. An organization must invest in a team of experts (data scientists) and create statistical algorithms for finding and accessing relevant data. The data analytics team works with business leaders to design a strategy for using predictive information.

 

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Achievements

Hill’s Pet Nutrition

Mr. Kuzanek has lead the re-engineering of 8 disparate information systems into an integrated environment aligned with the various busi