Business Intelligence | Seabrook Technology Group

Business Intelligence | Seabrook Technology Group

BI Strategies and Definitions

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BI Strategy & Definitions

In the previous Blog I spoke about the need for an effective Enterprise Intelligence strategy. In this blog I want to delve deeper into what a BI strategy is and the key models that make up a successful system.

Business-Intelligence-Concept-300x200When looking at the overarching strategy, people usually start with KPIs (Key Process Indicators / Key Performance Indicators) that show how an organization is performing. Often, these KPIs will be related to financials, efficiency and/or yield. It depends on the particular target area you are putting your Business Intelligence in and it would also encompass any definitions. For instance, some people calculate yield differently and therefore it would use the target area’s definition for yield calculations. Some want global yield, some want process yield and some want to know overall yield through multiple operations. They want to accumulate this knowledge to understand min/max lead times for reasons such as informing their customers of the time difference from ordering to receiving a product or how quickly do they need to be responsive to the change in market. This strategy and solution do not have to be solely focused on manufacturing processes; It could encompass many business functions such as quality release, order processing, planning, supply logistics, etcetera.

What is also important here is to take an inventory of your existing systems. A lot of organizations already have some systems collecting this data. A company wants to leverage those systems as much as possible (if they are desirable and serving their purpose adequately). An organization must identify where those systems are located and what they are recording in order to understand what data is being collected and potentially how much redundancy is there.

Due to the proprietary nature of these disparate systems, it is also important to develop Canonical Definitions to clear up any discrepancies across business units, systems, etc. so that every system is speaking the same language at the Business Intelligence level. In other words, harmonization of languages across your applications is essential. For instance, the details in a Production Order must be understandable by everyone in the organization at all levels.

The Canonical Definitions are to be used in your BI Models to insure proper contextualization of data and that each application is providing the appropriate level of detail in order to ascertain the appropriate context for the data provided.

Defining an action plan is critical to insure clear expectations are set for the use of the data. Some organizations try to implement a data warehouse and reporting tool hoping it will somehow catch on and people will adopt it and just start using it. However, if you do not define the actions initially, how do you know the right data and tool was selected or if it is even adding value? Once the “new” wears off, it loses enthusiasm and is just another application for your IT group to support. By defining an action plan, not only are expectations set, but requirements for the solution to insure it meets the overall objectives of the BI Strategy are also built. This can be important especially throughout the vendor selection and project post-mortem assessments as the solution can be measured against these core objectives and anticipated objectives for the target area.

Even though the Enterprise Intelligence Strategy is defined in a holistic manner, it is important to constrain the scope for each phase to enable you to roll out the BI solution and develop a very clear action plan in a controlled manner without losing sight of the objectives.The strategy should include a Scope and Phase definition in addition to the applicable action plan for that given phase. Begin with a specific target area in an organization to start the rollout, then list subsequent rollouts, leaving room for improvement after each implementation to allow for continuous improvement and expansion of the solution as the scope expands. This is ideally executed in an iterative cycle – PDCA approach (which is a lean methodology for Plan, Do, Check and Act).

It is ideal to start in a specified target area. Plan what you are going to do. Do it (execute the plan). Check  and evaluate the results at the end of the phase. Make adjustments and Act appropriately to improve when you move on with the next iteration or phase. Then based on what was learned and experienced, start a new iteration with a refined plan. Therefore, organization’s solutions are improving in addition to their associated processes, through each iteration.

If a company attempts to do everything at once as a “big bang” approach, it will more than likely fail. The processes will begin to adapt and improve requiring the solution to evolve as well. We have witnessed greatest results when a company allows the solution and the process to evolve together.

A typical action plan iteration will address the following:

  • What is to be measured, used, and communicated
  • What will be open for discovery models
  • Alert & Notifications scenario definitions
  • Business Rules for automated responses and actions based on certain KPI violations.


BI Models

When evaluating the Business Intelligence models to create and use, it is imperative to understand the intended use and type of data used in order to accurately define the capabilities needed from these models. The best way to understand this is to take a look at a few example models and understand how they can relate to specific initiatives and needs.

  • A KPI model
  • A Static Aggregation
  • A Discovery Model
  • A Predictive Analytics Model



Check out the next Blog to see how Applications play a critical role in an Enterprise Intelligence Solution and can greatly assist with any continuous improvement activities. 

Enterprise Intelligence: What is it and Why do you need it

Enterprise Intelligence: What is it and Why do you need it

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It seems every organization has its own definition of what Enterprise Intelligence might be – some organizations claim it is an educated work force, others may say it is an application such as business objects or Microsoft analytics reporting capabilities, the SASS or Cognos or whatever other applications people use for data analysis. Some organizations claim Enterprise Intelligence is the ability to put their metrics on big screens so everyone has visibility to the overall health of the process. Some people say it is the historical data that they have, the equipment level data or the product level data, or that it is the customer data that really encompasses Enterprise Intelligence.

I have concluded that it is all of the above and more. It is really about turning all of that data into value. We have found that, independently, none of these aforementioned claims really create value. It is only when one can obtain appropriately contextualized data, through the appropriate medium and couple it with the right technologies and business systems, can they really start to turn that data into prime value for the organization. Because of this, I prefer to define it as:

“The Execution of a Technological Strategy enabling integrated process and environmental knowledge to be leveraged in standard practices to accomplish operational excellence.”

When I refer to “environmental knowledge” in this definition, I refer to all of the environmental knowledge – the individual employee knowledge, the experts on the particular process, the product and manufacturing data and market data. By pulling all that data together and leveraging it into standard practices, you can achieve operational excellence.

In a 2012 survey the question was posed which were the most important challenges that faced organizations’ supply chains. At the top of the list sat product launches followed by increasing portfolio complexity and regulatory scrutiny.

There is evidence that key business drivers impacting manufacturing are innovation, followed by manufacturing agility and manufacturing complexity all while being required to maintain regulatory compliance.

So, what does this really mean? It means companies really have to provide better products (which are more complicated to manufacture) and we need to do it faster. Not only do they need to design these products faster, even though they need to be more complex while creating more value to their customers, but they have to do it under tighter regulations. This is especially true for life science companies where one cannot just have a good idea and create a product. A product now has to be characterized, it has to be proven and it has to be validated. These tighter regulations create a higher cost to organizations but the market demands companies to manufacture products cheaper in order to remain competitive. There is huge erosion in the profitability of these markets today. Consequently, things need to be done more efficiently and cost effectively to provide a product that the customer is willing to pay for while maintaining some level of profit margin for the manufacturer. The need for a better way is evident. How does an organization juggle all of these market and regulatory demands? How does an organization know where efficiency improvements can be made? How does an organization visualize the waste that may be in the processes and supply chain? – Enterprise Intelligence is one likely answer to all of these questions.

Look out for the next edition of this Blog Series – An Enterprise Intelligence Strategy

Author: John Dzelme, Industry Recognised MES & BI Expert

Setting the Stage – Enterprise Intelligence that Drives Operational Excellence

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“After all, data that doesn’t do anything is just data; Data that drives action… is intelligence.”

It is important to understand the hype behind Big Data and the realities of how companies can utilize Big Data to create value. This initiative can be broken into three segments. One part of this blog series will be covering the different aspects of Business Intelligence. The second part will include concepts on how technology can be integrated and leveraged to create maximum value and operational excellence, but only with proper controls in place to insure quality and compliance is enforced. The third branch of this blog series is a realistic case study to show what this Enterprise Intelligence Solution can really look like in a practical sense and what type of return on investment one can expect, if implemented properly.

ROI and Enterprise Intelligence

The hype behind Big Data really is driven by the potential value that Big Data can provide for organizations. The basic premise is that with the appropriate enterprise intelligence strategy and appropriate complementary technologies, an intelligence solution can provide significant returns on investment. Organizations are interested in intelligence capabilities and how Big Data allows them to have a competitive edge or how it can drastically reduce cost of goods sold. It is also important to understand the different stages of data and how data can be leveraged within an enterprise for specific initiatives, especially those that promote and enable a continuous improvement culture. The danger and horror stories around poorly executed Big Data implementations are typically those that do not take a holistic approach to the solution. Instead, some organizations make the perilous mistake of focusing on just the data, or just the technology, or even just a specific application. Unfortunately (contrary to popular belief and application sales literature), there is not a single application on the market that can provide a complete end-to-end Business Intelligence (BI) Solution. An organization’s data is spread amongst many data sources with different contexts. In addition, the real power of data is lost if it is not capable of being used in applications for decision making.

Implementing an Intelligence Strategy

Real-time Enterprise, Big Data, Manufacturing Intelligence, and Enterprise Intelligence, along with a host of other “buzz” words are really attempting to solve the same problem. The real limitation with each of these is that if an organization does not first set out an Enterprise Level Intelligence Strategy, any solution will have minimal gains. Numerous variables impact the actual use of this data from its intuitiveness and structure all the way down to the method of delivery. Many times the delivery is often overlooked, however even the medium used to deliver the message will make a difference as to whether or not the data will be useful or not. An organization must understand that one of the main objectives of an Enterprise Intelligence Strategy is that you are taking a holistic approach to the solution and leveraging the strengths of current investments as well as introducing new systems or technologies that will maximize the value that your specific data can provide. In addition, it is also important to understand that contextualization is an integral part of the solution design process and should be incorporated into the overall strategy to insure the solution is also providing a single version of the truth. Included in the following whitepaper is a brief coverage of the specific limitations of an application-centric approach, why there is a need to first adopt a larger perspective before selecting your technology, and how to understand enterprise level intelligence at the macro level. The importance of incorporating manufacturing technologies to protect your current investments, provide advanced intelligence capabilities and mitigate risk (especially during improvement activities) will also be covered. Most importantly, this should enable one to understand how the business models themselves can be coupled with integrated platforms to be leveraged to drive, foresee and even automate actions.

Revolutionary Times

Just to point out, these are revolutionary times. Some of the younger generation reading this text may not see that a revolution is happening, but we have the privilege of working with some of the most innovative companies in the world, primarily in the Life Sciences sector. Some of Seabrook’s clients are not just changing their organization with this data but they are rocking the entire industry. Some of them are not even changing the industry; they are changing the world. We have organizations that are enabling people to manage their illnesses better, or, even curing those illnesses. Some of our clients are providing genetic technology that allows us to analyze things at such a molecular level that we can actually grow nutrient-rich vegetation in the harshest climates. We are potentially talking about solving world hunger. Therefore, we see ourselves as privileged to work with these companies. This data is more than making a profit; it can literally enable us to change the world. There is not one pill you can take that will solve all of your organization’s problems. More and more, organizations are coming under tighter regulatory scrutiny. More advanced technologies are required to manufacture products and customers themselves are requiring better electronic products and more complicated pharmaceuticals. All of these requirements are creating challenges for the Big Data concept and the technologies that accompany it.

Look out for the next edition of this Blog Series – Enterprise Intelligence: What is it & Why do we need it.

BIG Data & Business Intelligence

Big Data & Business Intelligence

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There is so much discussion in the manufacturing industry about Big Data at this moment in time. Some of it is myth and hype, and some are real examples of how data can be used to add value to an organization. Seabrook are delighted to announce the start of a blog series by industry recognized Business Intelligence & Big Data expert, John Dzelme. The 6 part series provides insight into how organizational data can be used to not only provide value but return huge savings to an organization. Read More