Implementation is the realization of an application, or execution of a plan, idea, model, design, specification, standard, algorithm, or policy.
An implementation is a realization of a technical specification or algorithm as a program, software component, or other computer system through programming and deployment. Many implementations may exist for a given specification or standard. For example, web browsers contain implementations of World Wide Web Consortium-recommended specifications, and software development tools contain implementations of programming languages.
Implementation can be viewed in terms of four typical phases:
- Planning and discovery: Defining the need(s), deciding on solutions, detailing what to do, how to do it, and who will be involved.
- Project preparation: Risk assessment, budgeting, purchasing, staffing, establishing project management, etc.
- Development: Requirements, installation, coding, documentation, testing, etc.
- Deployment: Training, roll-out, support, etc.
The implementation of a BI project can also involve product evaluations, vendor management, project communications, retirement of legacy systems, and many other activities. In short: There are a lot of moving parts.
By some assessments, the majority of BI projects fail. Some only fail to meet the highest expectations, others crash and burn completely. Among the frequent problems:
- Users don’t like what’s been implemented and refuse to use it.
- What’s been implemented isn’t what was really needed.
- The project goes far over budget.
- The project takes too long to implement, and is obsolete or redundant by the time it rolls out.
Why do these unhappy outcomes occur so often? Top reasons:
- Insufficient planning and/or discovery
- Lack of executive support and/or user education
- Poor change management and/or communication
- Poor data quality and/or slow performance
- Poor design and/or wrong tool(s)
- Inadequate budget and/or unrealistic expectations
These problems can occur with in-house projects (often because employee resources are not up to the required tasks), and also with outsourced projects (often because the external resources don’t understand the business). In short: There are no safe choices or easy answers.
These potential pitfalls are not new. In fact, the same issues have been identified and discussed for years. There are many books, workshops, and consultants that promise to explain the best practices of BI implementation—and for the most part, they all offer similar and familiar advice. So why hasn’t the success ratio of BI projects improved?
One reason is that the good advice either isn’t understood or isn’t followed. Many organizations implement best practice “labels” without actually understanding and adopting the practices. Other organizations may follow some best practices and ignore others, which almost never works out well.
But an increasingly important cause of project problems may be that traditional BI implementation is no longer the best approach, at least for some projects and some organizations. Alternative solutions such as agile BI and self-serve or cloud-based BI may offer a better way forward–which means that a new understanding of best practices will be emerging.
Success factors of implementation
Before implementing a BI solution, it is worth taking different factors into consideration before proceeding. According to Kimball et al., these are the three critical areas that you need to assess within your organization before getting ready to do a BI project:
- The level of commitment and sponsorship of the project from senior management
- The level of business need for creating a BI implementation
- The amount and quality of business data available.
The commitment and sponsorship of senior management is according to Kimball et al., the most important criteria for assessment. This is because having strong management backing helps overcome shortcomings elsewhere in the project. However, as Kimball et al. state: “even the most elegantly designed DW/BI system cannot overcome a lack of business [management] sponsorship”.
It is important that management personnel who participate in the project have a vision and an idea of the benefits and drawbacks of implementing a BI system. The best business sponsor should have organizational clout and should be well connected within the organization. It is ideal that the business sponsor is demanding but also able to be realistic and supportive if the implementation runs into delays or drawbacks. The management sponsor also needs to be able to assume accountability and to take responsibility for failures and setbacks on the project. Support from multiple members of the management ensures the project does not fail if one person leaves the steering group. However, having many managers work together on the project can also mean that there are several different interests that attempt to pull the project in different directions, such as if different departments want to put more emphasis on their usage. This issue can be countered by an early and specific analysis of the business areas that benefit the most from the implementation. All stakeholders in project should participate in this analysis in order for them to feel ownership of the project and to find common ground.
Another management problem that should be encountered before start of implementation is if the business sponsor is overly aggressive. If the management individual gets carried away by the possibilities of using BI and starts wanting the DW or BI implementation to include several different sets of data that were not included in the original planning phase. However, since extra implementations of extra data may add many months to the original plan, it's wise to make sure the person from management is aware of his actions.
Because of the close relationship with senior management, another critical thing that must be assessed before the project begins is whether or not there is a business need and whether there is a clear business benefit by doing the implementation. The needs and benefits of the implementation are sometimes driven by competition and the need to gain an advantage in the market. Another reason for a business-driven approach to implementation of BI is the acquisition of other organizations that enlarge the original organization it can sometimes be beneficial to implement DW or BI in order to create more oversight.
Companies that implement BI are often large, multinational organizations with diverse subsidiaries. A well-designed BI solution provides a consolidated view of key business data not available anywhere else in the organization, giving management visibility and control over measures that otherwise would not exist.
Amount and quality of available data
Without good data, it does not matter how good the management sponsorship or business-driven motivation is. Without proper data, or with too little quality data, any BI implementation fails. Before implementation it is a good idea to do data profiling. This analysis identifies the “content, consistency and structure [..]” of the data. This should be done as early as possible in the process and if the analysis shows that data is lacking, put the project on the shelf temporarily while the IT department figures out how to properly collect data.
When planning for business data and business intelligence requirements, it is always advisable to consider specific scenarios that apply to a particular organization, and then select the business intelligence features best suited for the scenario.
Often, scenarios revolve around distinct business processes, each built on one or more data sources. These sources are used by features that present that data as information to knowledge workers, who subsequently act on that information. The business needs of the organization for each business process adopted correspond to the essential steps of business intelligence. These essential steps of business intelligence includes but not limited to:
- Go through business data sources in order to collect needed data
- Convert business data to information and present appropriately
- Query and analyze data
- Act on those data collected
It includes the following topics -