What is Business Intelligence?

In 1958 a researcher from IBM introduced the term Business Intelligence along with its interpretation as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.”

Business intelligence (more commonly referred as BI) refers to an application or a set of applications implemented by skills and technologies to help an organization to get better insight of commercial context. It may also refer to the collected set of information inclusive of statistical data.

Warehouses and Data mart are often used for data gathering by BI applications. It is not all the time that all BI Applications require a data warehouse.

How Business Intelligence is so useful?

BI technologies in combination of software application provide an organization a predictive layout of whole day to day functioning. It also provides current, legend and business operation information to the management.

Most common functional area of business support system :

• Reporting
• OLAP
• Analysis
• Benchmarking
• Text mining
• Data mining
• Business performance management
• Predictive analysis

Also, as a part of technicality, some large scale industries use their own platform to create the application which small and medium scale organization use .net framework library to create such applications.

Many changes has recently been done in form of architecture used for such applications. Now a days in 2.0 version of application SOA service oriented architecture, which enables for a flexible, composable and adaptive middleware.

The main aim behind Business Intelligence Applications is to provide an organization better planning and decision making. Thus BI Systems are sometimes referred to a Decision Support System (DSS). DSS is widely used by top level management who deals with MIS.

The customer relationship management (CRM) and business intelligence can sometimes conflict because your CRM tool has your customer activity from a sales perspective, and your data warehouse has you customer activity from an operational perspective in it, so these two systems double up the information required to do their respected jobs, this means the CRM system will have prospects and sales and marketing data in it that usually will not end up in your data warehouse (because it isn’t interested in the mailing lists you procure, and the vast amount of data that you’re marketing to), it’s more interested in what the business is doing with operational information.

At the same time, to run a successful marketing campaign, you need transactional history of your customers to ensure your campaigns are correctly targeted and this sophisticated data analysis and history transactions is usually in the data warehouse.

The challenge this brings is ensuring the data between these two systems is managed in a way that means the activities that come back into the data warehouse can measure what comes in the operational systems. This way you always know what money you’re making from your marketing activities and you can send these customer data sets back to your CRM system.

If you’ve implanted an enterprise resource planning (ERP) solution, you’ve already gone a long way to integrating your systems, unfortunately, just because you’ve got an ERP solution doesn’t mean you don’t also need a data warehouse.

ERP solutions are package solutions, and this means that it’s a ‘one solution fits many’ and therefore they need to be configured and tweaked to ensure they match your businesses individual needs and this can make getting the right information out of them slightly awkward, especially if you’ve had to bolt a management information system onto the side of your ERP solution.

You need to then decide whether you put that data in your ERP system so it then becomes your data warehouse to give you an enterprise view, or do you take your ERP data and put it in a data warehouse along side your other pots of data and make that your enterprise view? This is something you’ll need to address as part of your IT strategy.

More organisations are using software as a service to implement their core systems, this is where by you use an online service provider to host your solution for you and access it through the web, this does mean your data is no longer inside your organisation and as it’s held externally, you have less access to it.

If you think you need enterprise wide business intelligence that includes that data, you need to factor in getting a data feed back into that organisation and into your systems, you then need somewhere to store and analyse it. You also need to consider what kind of business intelligence those solutions provide to see how you’re going to progress with your business intelligence solutions if your going to use your software as a service.

For many years business intelligence vendors were fighting with the Office products like Excel, but now they’re recognised as being just as integral to business intelligence as any other piece of software or solution. Everybody uses it all the time, the finance department love it and as Microsoft gets the technology and the visualisation capability within Excel improving it will probably be used even more.

Background and History of Business Intelligence:

According to Wikipedia, the term ‘business intelligence’ was first used by IBM researcher Hans Luhn in 1958. He defined it as ‘the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.’

Since that time, business intelligence (also known as ‘BI’) has developed and grown, and nowadays when the term ‘BI’ is used it generally refers to the gathering, storing and analyzing of data for the purpose of making intelligent business decisions.

Purpose of BI Technology:

Most (if not all) businesses deal with huge amounts of data. Analyzing this raw data in a quick and accurate manner is extremely difficult due to key trends that are masked by the sheer amount of information to digest.

This is where business intelligence software and technologies can help. BI provides a proven, well-defined methodology to process and analyze business-related information quickly and accurately. This is accomplished via the BI ‘stack.’

Business Intelligence Stack:

The BI stack has been traditionally defined as follows:

1. Data Layer:

a. Consists of the raw data that needs to be analyzed.
b. Data can originate from multiple sources, such as: MySQL, MS SQL, Oracle and Access databases; OLAP (online analytical processing) sources; various spreadsheets like MS Excel; CSV files; and even data sources that are not structured.

2. Analytics Layer:

a. This layer is responsible for transforming the raw data into meaningful information.
b. Components that constitute this analytics layer can be:

i. Data mining: refers to the process of extracting patterns out of raw data.
ii. Predictive analysis: involves the analyzing of data and then predicting future events or patterns.

iii. KPI formulation: the formulation of key performance indicators (KPIs) which are meaningful to a business.

iv. And any other business-specific methods of transforming and massaging data.

3. Presentation Layer:

a. The presentation layer is responsible for visually representing the data provided by the analytics layer.
b. Data visualization can be accomplished via digital dashboards, performance scorecards, graphs, reports, gauges, indicators and any other visualization components.

To summarize how the BI stack functions:

1. Data is collected from a variety of sources.
2. The data is then transformed into meaningful information.
3. The massaged information is displayed to end users using data visualization methods.

The Future of Business Intelligence:

BI technology is constantly evolving and this is reflected by changes to the BI stack.

It is important to note that the stack is not ‘set in stone.’ The nature of business varies to a great extent and how a company chooses to implement business intelligence in their decision-making processes will affect their implementation of the BI stack.

Recent modifications to the stack include things such as:

• The mass adoption of mobile BI, which is reflected in the presentation layer.
• Major advancements in predictive analytics, a component of the analytics layer.
• Environmentally friendly data storage.

Business Intelligence for Dynamics AX – What is the Best Solution?

This question will be asked every day by thousands of Dynamics AX users and many of their AX partners. There is not one answer to this question as it really depends on many factors like size of the company, do you already use a kind of BI tool, your preferred platform like SQL Server or Oracle and many more. Without any doubt, a Business Intelligence Solution without an underlying data warehouse is doomed for problems, at least in the long run. Some of your options are:

1. Build your own data warehouse and Business Intelligence Solution with Microsoft BIDS

2. Use embedded Business Intelligence by Microsoft in Dynamics AX

3. Purchase a pre-packaged Business Intelligence Solution which is tailored for Dynamics AX

4. Purchase a drag and drop data warehouse tool which auto-generates your SQL code and also builds your OLAP cubes and select an Analysis tool of your choice.

A few comments to these four options:

Microsoft BIDS (Business Intelligence Development Studio) is a great development environment for SQL Server experts to create a SQL Server based data warehouse and OLAP cubes. So if you have the experts in-house and want to spend the time and money, this is an option, but be aware of the complexity of BIDS, the time it takes to create a BI solution with it and the dependency on these experts, either in your own company or externally.

Microsoft Dynamics AX offers pre-packaged BI components like OLAP cubes for a variety of AX modules. But be aware that they are not based on a true data warehouse which means any integration of external data sources or changes in dimensions/measures/KPI’s is possible, but complicated and needs experts again. It is generally not a good idea to integrate Business Intelligence into ERP. There are many pre-defined Dynamics Business Intelligence Solutions around, like ZAP, iQ4bis, Profitbase, Brio, Qlikview, Targit and many more. All of them have their benefits to implement a BI solution for Dynamics relatively fast. The only reservation I have is, that they either don’t offer a true data warehouse as the underlying technology forces you to use their propriety front-end tool or both. The price difference for these tools varies from $15,000 to $100,000+

In my opinion, option 4 of the above list will give you the best results and highest flexibility. There are not a lot of tools around which helps you to create a flexible and ERP independent data warehouse and also creates the OLAP cubes with little effort and is front-end agnostic. Why not start with technology you already use like SQL Server, Excel and SharePoint and then look into Analytics tool later.