Not Just Simple Reporting

BI type solutions, of which CPM is a key part, are going through a transformation in the corporate world currently. For many organisations BI meant operational and management reporting reporting through simple reports and some dashboards, with low level aggregation of the data in response to many queries. In part new demands drive an understanding of  not simply what happened but why it occurred, which in turn can assist in predicting future actions.

One of the key aspects today is the movement to adopt predictive analytics. For many this is the new goal, yet in reality predictive analytics have been available for more than twenty years, it has however always been complex to implement and requires very knowledgeable users having a strong background in mathematics and statistics. Knowing that there is a correlation between TV program times and people’s shopping patterns may enable a supermarket to plan their staffing shifts appropriately, bringing in staff to cover the store peak times.

Self Service?

Another challenge here is in moving towards more of a self-service model for information delivery with the intent of lowering the total cost of ownership, yet still provide data to the business community in a timely manner. The challenge here is of course the sheer volume of data available, which is why many BI solutions have traditionally been only able to tap into a small section of it. Business challenges like tracking of orders are now becoming a BI initiative, rather than something managed through the ERP solution, thus it is vital the BI database receives continual and timely updates. The advantage here is that the BI system contains all the data whereas in many corporations operational processes are frequently split between several systems.

The delivery of data from one system to another still requires technical expertise. Data availability should be taken on a step by step evolutionary basis, each new element enhancing the existing data model. Yet this data provision is still fulfilling a specific business need, and it is important that business managers are actively engaged in the process. It is clear today that the demand for business metrics is unable to be met by loading data into Excel spreadsheets (Excel was never intended to handle large complex models). Part of making BI a success in ensuring that the business community recognises that in order to fulfill their reporting needs it is necessary to go beyond the limits of the spreadsheet. Database models have always been able to be as complex as necessary to handle the required solution, yet the BI databases provide simple to understand structures yet provide powerful capabilities in order to drive operational, management, and strategic reporting and queries.

Social Media Impact

One other aspect is the need to measure the impact of blog posts and any corporate social media presence. What kind of penetration is certainly something that should be measured. Yet there is another aspect here, called Social Media Intelligence, which should not be confused with traditional BI, yet in itself it can feed any BI solution. The goal of Social Media Intelligence is to seek out any mentions of you brand out in the multitude of social media channels. The intent behind Social Media Intelligence is to act as a listening station in order to make the corporation more responsive to its customers on each of the social channels. It is also a way of monitoring what is being said about your competitors.

How to respond is a challenge that will depend largely on the popularity of the poster. Something said by a person with only 12 followers may have little consequence when compared to the person having 12,000. The Social Media Intelligence is an additional data source that when used right can give some insight on the influence your brands wield in the marketplace – the trick with this intelligence is turning something textual into something measurable.

The Advantage of BI

To be clear Business Intelligence is a mature technology, it has existed for for than a quarter of a century, with this writer being involved in some of the earliest BI applications. Regardless of where a corporation is in respect of BI it is crucial to understand that, when used correctly, this is a powerful tool that can help any corporation make smarter decisions based upon the information at their fingertips.

Normally this is achieved by combining  the use of advanced analytics with analytical CRM components. This is one form of closed-loop Business Intelligence, the principle of which is to predict behaviour based on classes of customer. Of course a class can be encompass a group of one person, but is normally much larger. The concept is that each customer would fall into certain demographic groups. There are certain behaviour patterns that could be drawn from the generalised behaviour of specific groups of people. We are now living in different times and whilst group behaviour can still assist in determining the general needs of a group it does not assist in knowing why individual customers make specific choices.

Take Joe, he is 52 years old, middle class, owns a Mercedes on many things he is typical for his age group, a true red-blooded American, supports his college football team. On the management team where he works. He is involved with many local causes. A family man with three kids, staunchly conservative on most issues, yet a vegetarian since his youth. His whole family have followed him in this regard with each making a conscious choice.

When the local supermarket sends them special offers it is always for meat products that nobody in the family eats. This is a case of the store fitting him and his family into specific demographic groups and having little tolerance for individual preference within their data structures. For most things Joe and his family follow the norms associated with their demographics. Through traditional Business Intelligence it is possible to identify specific anomalies to demographic norms, yet there is a tendency to believe that Joe’s family purchase their meat from another store; hence the offers that are sent.

UK supermarket chain Tesco has performed much research in making special offers relevant to their customers, yet it is all too easy to make assumptions. Thus it is all too easy to misunderstand why people do the things they do. We may never truly understand why people do all they do, even with a psychologist on the business team.

What social media adds to the picture can be the views and attitudes of the customer. However to add value these must drive additional insight over and above the demographic norms. For small businesses there is often more value driven by listening to customers than can be gleamed from analytics. Of course small businesses have to think twice before adding expensive systems.

All businesses want repeat business, we have heard the maxim of it being easier to sell to an old customer than a new one. There is much psychology and academic study in the area of repeat business. The last of these is often forgotten, but can drive so much business value.

Over the last couple of years there has been much discussion about how there is no such thing as loyalty any more. Particularly as the youth, or gen-Y and millennials, as some insist on calling them. Yet this writer has observed that loyalty is still important; it is simply driven by a different set of values than at earlier times. The value system today includes friends and on-line social contacts, they all have something to say.

The psychology of the customer has changed a little. Today the customer knows they are in command of the relationship. The psychology of buying is all about trust. This is one of the keys behind much of the move to adopt social media by such a wide audience. This is one reason why any business that is looking to build customers needs to think about engaging through that medium. it is all about demonstrating expertise and building trust.

There is much business intelligence to be drawn from loyalty programmes. Knowing what purchases a customer makes in order to drive appropriate offers is a key aspect. A supermarket sending money off tokens for meat produce makes no sense to a vegetarian. How does the store know they are vegetarian? Well one way may be through buying patterns – if they have never purchased a meat product that may be a clue, another method is through surveys – all add to the details we know about the customer. Relevance of the reward is an important factor for the customer.

Remember for a rewards programme to be successful it must focus on the needs of the customer.

The addition of new technologies, like cloud computing, and social media we have new ways to access and publish important data and statistics about our trade or industry sector. Our ability to make good decisions should largely be based on the data that can be obtained about the subject at hand, and on the analysis of results identified. This should be the basis for the most informed decisions made, yet many of the business managers who interpret the results all too often prefer to use their own gut-instinct than believe the report in front of them.

Is it natural? Yes, but that does not make it right. If a business is going to invest millions of dollars into its data and information systems over a long period of time ensuring automated processes are closely linked to business activity then ensuring that the results are taken seriously is an imperative. To assess a set of results and dismiss them is one matter, but to ignore them altogether is another.

The latter course is the dangerous path that a large number of managers find they are taking because they do not have time in their schedule to understand the results. This is a case of making time to ensure that the groundwork is laid before the critical decision must be made. For example are all those meetings necessary? If they are then can someone else attend? Or can the work be done another way? It is necessary to step back and understand the analysis before making that decision. Remember in most non-critical matters 80 percent of your return comes from 20 percent of your effort and often inordinate effort is spent attaining perfection, when it need not be. Non-critical matters can always be temporarily put on the back-burner, or delegated, when time is of the essence for a critical decision.

Business is in a state of continuous improvement. Look at the history of any corporation, it is unlikely to be run the same way today as it was twenty years ago. Yet that seems to be the way in which some decision are often made. For that improvement to be truly effective across the corporate culture then informed decision making needs to be a part of that picture. Some elements that require thought:

◊ Informed decision making requires more holistic thinking

◊ Sound science is a critical component of sound decision making

◊ The scientific results are a means to making informed decisions, not an end in themselves

◊Provided with reliable information and reliable tools to process it, people will make decisions that are good for themselves and their corporation.

Armed with some thinking in each of these areas it is possible to make better use of the information available throughout the decision making process.

For Business Intelligence which type of solution is best will ultimately depend on individual business circumstances. The solution type assessment that has been discussed in this series of articles will aid that decision.

We are in the age of careful spending. So companies need to know that each dollar spent is going to provide business value. It is important to develop a consolidated global view irrespective of operational software used. Consolidating data into a common platform is not always easy. The Data Warehouse must always be the single source of the truth for corporate information. This must be based on an open and flexible architecture that is extendible. To be clear, whether the data warehouse database is a bespoke design or implements a packaged solution there is still a large amount of work to be completed. There will always be the need to build a project team and acquire data for the data warehouse.

When ready made solutions provide best results

Generally ready-made solutions provide best results when they offer a high degree of integration for existing operational applications. If using a corporate-wide ERP or CRM solution that provides the backbone tying all corporate applications together then it is possible to utilise the associated data warehouse as the corporate Business Intelligence backbone. The preference for a pre-built database can be strengthened if all of the applications identified can be provided by the same vendor.

Ready-made solutions are becoming increasingly complex as the solution providers learn and adapt their data models based on real business scenarios, these will continue to evolve into the future.

What custom solutions can provide

Custom-made data warehouses are capable of supporting any application that is required to run against it. The primary concern will always be the amount of time necessary to develop any solution. Even where packaged Analytic applications are to be used is it generally necessary to tweak the metadata to enable them to function smoothly against the database. Often these Analytic applications can best be supported through a custom data mart, fed by the data warehouse – the single source of truth.

When custom solutions provide best results

Custom solutions provide the best results when there is a high degree of complexity in the corporate legacy architecture, even if there is an intention trim and simplify the architecture. Custom solutions are also best when the solution itself is likely to be unique in nature, e.g. when your corporation is genuinely the only corporation in the world doing this – however this situation is increasingly rare in today’s business climate.

Five years ago the combination of complex business processes and a complex architecture guaranteed that customised solutions were necessary. Today this is less clear cut. look at some industry models and see whether it is possible to adapt them before making the final decision.

Leveraging the Solution Type Assessment

In the earlier articles the author talked about a Solution Type Assessment. Through this process it is possible to build a score based on firstly mission criticality, and secondly the complexity rating for your organisation. These will act as a weighting factor for the importance of each item covered in the decision process.the weighting factor should be agreed before starting the assessment. In weighting there is a tendency for business to show a low weighting factor for IT issues, this MUST be avoided as the technical obstacles do need to be considered.

The Complexity to Provide column relates to the ability of the current data processing facilities to provide such data in a succinct and meaningful way. It becomes complex if data is stored in many different systems across the world, and in different languages. It is also complex if many types of data storage system are used (e.g. non-relational data stores and relational data bases can provide parts of the data).

Overall taking a solution type assessment will ensure the necessary due diligence considerations are covered. Of course capability has to assessed alongside the overall project costs. Customised solutions can be implemented over a longer period of time whereas ready-made solutions will generally be implemented altogether, even if some data comes on-line at a later date.

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This series will be taking a break for a few weeks, but when it returns the next article will focus on: “Staffing your BI Solution”

Previous:

Part 1: Financing the Business Intelligence Project (Buy or Build?)

Part 2: BI Project Decisions: Ready Made Database Options

Part 3: The Corporate Architecture and Complexity of Data Links

Part 4: Financing the BI Project: Uniqueness Factors (or USP)

Part 5: Starting your BI Project: Suitable Analytical Applications

In this context the adoption of analytical applications includes general reporting requirements (e.g. monthly sales reporting) as well as specialist applications (for example Activity Based Costing). This requires a projection of future need, but is still essential to determining the necessity of a pre-built Data Warehouse. Some applications will be tied to a packaged solution, whilst other options will have general availability.

Indeed when you are defining a ‘greenfield’ business intelligence implementation with a corporation that has few skills in the area it can be difficult to think about advanced analytics. The problem here is that some individuals will hail from other corporations that are analytically rich and these individuals want to launch into advanced analytical applications, when the traditional groundwork has not been laid out (e.g. a long history for analytics and a knowledgeable analytical team in the business).

Having recently worked with a growing mid-sized corporation who had recently appointed a new CFO to assist in their growth, she of-course came to the corporation bursting with ideas that had assisted her former employer to success, yet in the new environment there was no analytical capability and to make matters worse there were two accounting systems. She had a challenge on her hands, none the least being how she could implement the cost analysis modules she required.

The solution assessment  requires ratings for all analytical applications. It is essential to indicate all applications that are intended to be used in the future, even if this picture is currently uncertain. for example:

◊ Monthly financial reporting

◊ Ad-hoc queries

◊Customer Profitability

◊ Product Profitability

◊ Activity Based Costing

◊ Balanced Scorecard

◊ Inventory Analysis

◊ Business modelling

Identifying basic applications is as important the more advanced. It is essential to focus on those elements that are mission critical right now as well as its complexity to achieve. Complexity here is based on a number of factors including the immediate availability of this data. Where data components are not available today then the complexity goes up as the corporation has to consider how to obtain this data.

As an observation the reader will need to analyse the range of applications that are provided in association with any pre-built Data Warehouse in order to assess its suitability. Be aware also that many applications are based on data marts, these are focused data sets that should take data from the existing data warehouse database and restructure it to suit the analytical application being implemented. This mart should never be confused with the role of the data warehouse.

Next: “Which Solution is Best?”

Previous:

Part 1: Financing the Business Intelligence Project (Buy or Build?)

Part 2: BI Project Decisions: Ready Made Database Options

Part 3: The Corporate Architecture and Complexity of Data Links

Part 4: Financing the BI Project: Uniqueness Factors (or USP)

The last article in this series covered “The Corporate Architecture and Complexity of Data Links“. This builds on out business understanding that impacts the selected solution. You should walk away from this series with an impression the selecting a BI solution is not merely about software selection, but about a whole range of factors that require a more holistic vision about how the corporation will be leveraging its business intelligence capability. Yes we do need to measure profitability, but ultimately we also need to measure a range of other factors.

So, what makes our business unique?

Each business has a set of products or services. Many of these may also sold by the company’s competitors, at a similar price in a similar format. There however is always some distinguishing feature that ensures customers buy our product or service rather than our competitor’s. This may be as simple as undercutting the competition’s price or may be some unusual aesthetic feature. Knowing the factors of uniqueness is essential for each business. This has been described as many things over the years, but it is generally known as the unique selling proposition, or USP.

Each business, of course, has its competitors. One of the reasons for implementing business intelligence solutions is to identify ways to take advantage of prevailing market conditions. Thus it necessary to understand the ebb and flow of the marketplace; how this industry sector(s) function. Marketplace goals will also be a driving factor.

One of the major reasons for introducing Data Warehousing technology is to identify and measure factors that distinguish this business from all of its competitors. Pre-packaged solutions are rarely capable of doing this. They concentrate on general business goals, or maybe that of the market sector. Uniqueness will always need to be added to any pre-packaged solution.

Note: the author has identified two factors that need to be considered:

►  Factors that are common within the particular industry sector. This may be all insurance companies, or alternatively may be insurance companies operating in the life and pensions sector, where specific rules have to be met.

►  Factors that make our specific business unique. E.g. the brand name – Coca Cola, the reputation for quality workmanship – Rolls Royce.

When I work with corporations to deploy BI solutions I have created an assessment form that allows these factors to be both defined and assessed for your corporation. If we take an example of a corporation involved in the travel sector some of the generic industry factors may be:

◊ Declining corporate travel market

◊ Buoyant executive travel market

◊ Growth of no-frills airlines

◊ Growth of on-line sales

◊ Reducing costs

◊ The impact of terrorism on the marketplace

Factors making our specific business unique may include:

◊ Guaranteed lowest price whether booked on-line of in store

◊ Value customer service and retention (follow up call after trip)

◊ Wide variety of sales channels, even active on Social Media

◊ Active in all areas of the world

◊ International free-call help line

◊ Links airlines and corporate customers.

Understanding these factors and classifying both their mission criticality and their complexity are both important to the financial decision making process. Some factors here may be regarded as complex because such data is not currently recorded within the company.

As a general observation the more unique the business the less likely it will be a candidate for using a pre-packaged Data Warehousing solution and the more likelihood that the corporation will have to define their own database model or make changes to an ‘industry focused template’.

Next “Suitable Analytical Applications”

This article is the third in the series on Financing the BI Solution and follows from “BI Project Decisions: Ready Made Database Options” and “Financing the Business Intelligence Project“.

The decisions we make about the solutions we buy, which must all be made at the start of any new implementation do require analysis of a wide range of factors. In the case of the BI solution these must consider the existing infrastructure and any proposed imminent changes to it. This does complicate the decision making, but it is an essential part of BI project success.

Within any Business Intelligence implementation it is necessary to understand the impact of the corporate IT architecture. The complexity of the architecture can be a key factor in identifying any plausible ready-made solution. Many organisations have a veritable spiders-web of systems and inter-relationships some of which will defy logic, yet exist they do. Whilst it is primarily the operational applications that will be providing data to the Data Warehouse that must be considered to support the solution must take a more holistic view. Any system may hold key pieces of information and is thus capable of providing data to the Warehouse.

If we examine any two companies we will become immediately aware of architectural differences between them (and these are based on real solutions provided in the past).

In one organisation there may be an evolutionary mix of systems of varying ages and types; with few systems integrated together; there are many overnight batch jobs that transfer data between systems, but no integrated approach. How the organisation was formed may have an impact on the solution needed: here acquisition has meant there are separate systems in each of 17 different countries, some are packaged solutions, but most are bespoke. For the global corporate headquarters to gather worldwide information it has to contend with a complex mix of old and new systems using different data storage standards; all potentially making this a complex solution.

Conversely a second corporations is more integrated: all applications use pre-built solutions, based on relational databases; bringing an integrated architecture, that is well documented, and has been at the foundation of its IT platforms. It should be clear to the reader that this company’s architecture is unlikely to be very complex.

It is essential as a part of the financial decision making process to grade both the business and technical complexity of the solution.

Integration of existing systems is always an essential step especially assessing the corporate legacy application architecture. Integration of systems is almost always ranked as one of the most important architectural factor when deciding whether a pre-built Data Warehouse can be implemented. Highly integrated systems can be an enabler for a ready-made database. They are not the sole reason to implement a pre-built solution.

It is necessary to add a word of warning before diving headlong into such an implementation. When companies introduce new ERP, or CRM systems there is immense pressure to introduce analytics at the same time. This can also be true for SaaS, or Cloud Computing solutions. Often this is the integrated ‘Business Warehouse’. When the new ERP or CRM is introduced it is unproven (from the business viewpoint) and there is an assumption that results taken from the out-of-the-can Analytical Application are valid and correct. This is not always the case. An assessment of data quality is as essential to the ready-made solution as it is to the custom built Warehouse. Generally a new system cannot have its data quality assessed until it is in production and has had the wrinkles ironed out of it.

Next article in this series “What Makes Our Business Unique?”

1. A full data model, usually linked closely with an operational application. SAP, Peoplesoft, Siebel and other software suppliers provide Data Warehouses linked with their ERP and CRM applications.
2. An industry ‘template’ that provides a basic model Data Warehouse relating to the industry sector that the business is trading in.

Remember this section is talking purely about the supporting database, the Data Warehouse, rather that the overall BI solution.

Additionally there are Cloud based BI, or BI as a Service, solutions that tend to have their own internal database, most are linked to cloud based solutions. Generally you should consider these to be included with the first choice here as they are normally related to other operational applications. However some cloud based solutions are not extendible, which may be a factor in the decision making.

This is a continuing article based upon: Financing the Business Intelligence Project (Buy or Build?)…

Full Data Warehouse Model:

Many software companies claim to provide pre-built, fully configurable and extensible Data Warehousing solutions. Few, if any, actually provide an ‘out-of-the-box’ solution for the Data Warehouse that is not linked to another application. Most are linked with other operational software, such as Enterprise Resource Planning or Customer Relationship Management, where the business will have committed a significant investment such technology and associated change programmes.

Solutions are likely to be tailored to industry specific business analysis needs and will probably include pre-configured ETL components that may include extraction software for other commonly used software packages. This can act as a considerable advantage as it can reduce Data Integration efforts.

The advantage offered is that where a business uses the supplier’s solution it can have a ready-made business intelligence offering ‘for a few dollars per head more’. The final solution is still customisable, but is linked into key enterprise systems. This is an excellent goal, but whether it is achievable is a question that must be factored into the decision process.

Industry Templates:

Bill Inmon, used to be a great believer in the use of industry templates, or generic data models. Other companies also provide customisable data models, focused on specific marketplaces. The general aim of these templates is to act as a teaser for a subsequent engagement of the supplier’s consultancy services in order to have that organisation complete the full Data Warehouse and Business Intelligence implementation.

Templates or generic data models are therefore an aid, or kick-start, to custom development rather than a full blown ready-made solution. They are discussed here because generic data models are sometimes seen as a solution rather than a step-up on the development process. The template, or generic data model, can provide a boost to the initial Data Warehouse development project. They can be used to:

♦  Provide a foundation that can be built upon

♦  Identify major data classes

♦  Identify common relationships

♦  Identify some common attributes within the data

♦  Identify some common keys and unique identifiers

♦  Get the modelling exercise off to a fast start

In other words, the generic data model is a good starting point for the creation of a corporate data model, upon which the Data Warehouse will be founded. Using the template or generic data model as the corporate data model is inevitably a mistake. They do not alleviate the need to think about the design of any solution; they simply provide a way to jump-start the data modelling exercise.

The template or generic data model is not really a ready-made solution it is really a short-cut to providing a custom solution. Such a model will not include any pre-configured ETL components so these factors have to enter into any decision made.

The Decision:

At the end of the day having a customisable model is important because the Business Intelligence capability should be able to map onto the organisation’s USP. These are factors that you need to use in order to make your purchase decision. The best models are visible and expansible.

It is easy to assume that there is a ready made solution available and today’s choices are becoming increasingly complex, because vendors are learning the lessons from work they have completed for customers in the past and they have therefore extended their models to include a large array of new capabilities. Some vendors have simply extended default models over time others make these capabilities available only for extra cost. Make sure you know all the cost factors before making any decision.

The Next Decision Factor: Your Corporate Architecture and the complexity of data

1. Whether to use a ready-made solution or have it custom built
2. What degree of external staffing resources are required

This is the first part in a series about financing your BI deployment.

In addition project costs will be impacted by:

◊ The focus of the solution; operational or strategic

◊ The type of architecture selected

◊ The development methodology

These are all issues that the decision maker needs to be aware of whilst working with the business to initiate the project. Financial justification is of-course vital to this process. It is essential that agreement is reached by the project board based upon the holistic needs of the business. How the business interacts with the project and the resulting solution will in the end determine the success of the implementation. This is a corporate-wide decision to make.

Some of the decision may differ in scale when extending an existing Business Intelligence solution, but on the whole the various aspects of this article all have to be considered.

Ready-Made or Custom Built?

This is a decision that impacts the structure of the underlying database, the Data Warehouse. In the past it was a relatively clear-cut decision that few if any solutions provided alongside new operational applications could offer anywhere near the complexity necessary to support strategic decision making. Their only contribution was in respect of operational decision making. The strategic decision making required more data to be aggregated from multiple systems in order to provide the right results.

Each packaged solution requires some customised data and user interfaces to ensure it fits each end-user business. The business is also expected to change its processes in order to work with certain applications. The use of ready-made OLTP solutions has made the task of hiring business community staff and training them easier. E.g. Job advertisements for an Accounts Administrator can specify SAP experience.

So can this experience be applied to the implementation of the Business Intelligence solution? Much depend on the factors making each business unique and the range of applications that will continue to be used within the organisation. Few, if any, of the true BI focused applications feature highly in any business community staff resumes, so training is always essential.

From the systems side a changing IT landscape during the past twenty-odd years where we prefer to buy rather than build encompasses the possible use of packaged solutions for both the Data Warehouse and the BI tool-kit. In order to make such a decision it is necessary to consider the impact of:

◊ The corporate architecture including the complexity of data links

◊ The degree of integration of existing systems

◊ Factors of uniqueness for the business

◊ The suitability of analytical applications

All of which can assist in assessing the suitability of potential ready-made solutions. In all cases it is important to understand the general goals of the business’s market sector, for example in some statutory controls will have a large impact and in others none. It is always necessary to consider what your corporation’s principle competitors are measuring, if only to ensure that you exceed those minimums. It would be great to have a sneak peek at the competitor’s solution, but often industry journals give an expose on what they are doing – these can be analysed to identify the factors being measured.

Another impact is the factors of uniqueness, e.g. what your corporation brings to the marketplace – each company has them, make sure you identify them.

The third factor that need to be considered is the existing legacy IT architecture. Many corporations have already purchased some of the technology that is intended to be used for the solution. Many systems come with data integration elements. From a pure cost effectiveness standpoint you should look at re-use instead of purchasing a new ETL solution. Most companies have them – it is a matter of whether they are fit for the new intended use. Another area to consider is pre-existing and spare licenses for existing analytical tools – many corporations complain about the cost of licensing, yet do not effectively utilise the licenses they already have.

The next article in this series: “Types of Ready Made BI Solutions”