The hottest analysis projects promote the smooth p

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Analysis projects promote the smooth progress of business intelligence

according to the results of the recent survey of CIOs, analysis projects are one of the current focus of CIOs. I think the most important thing to keep in mind if we want to ensure the smooth progress of such business intelligence projects is that they are fundamentally different from other IT projects. What are the differences? Here are some examples:

1 Analysis projects are very subtle

other IT projects, whether accounting or production planning, will follow a familiar process. Analysis projects are difficult to achieve this, because it essentially reflects the decision-making process, and human decision-making has no fixed path to trace

analysis projects are not used to implement specific business rules, transactions or workflows. We must always keep in close contact with the project owner, otherwise we will not be able to realize their ideas

2. Analytical projects may not follow the path originally envisaged.

no matter from the subtle and meticulous human decision-making process or the past successful project experience, we can conclude that analytical projects must follow the design concept of changing as needed. To some extent, this determines that there will not be a unified analysis project, but should adopt the agile mode - quickly create a prototype system and obtain feedback, step by step in multiple self-contained cycles and make random decisions

for example, our department was very stubborn about the end status of analysis projects. They are very clear about the ultimate goal and want to achieve it through an overall release. When I persuaded them to split the project into several dynamic and adjustable stages, they clearly responded that the development prospect of the product was 10 points optimistic: "it's not necessary. We know what to do. You can only provide the required data."

in order to avoid large-scale analysis projects, I proposed to them: let's use agile methods to complete the project and see if it is more effective. If I fail, I will invite the whole project team to lunch. Faced with this challenge, the project leader and I reached an agreement to conduct the test in the first phase

in the next few weeks, we completed the first phase and made the prototype of the product. All the members have different understanding and understanding of the project, the first is to promote the final output of research, development and utilization of new materials in the plastic industry. When they started planning for the next phase, their needs completely changed. Originally, I just wanted to collect information related to product feedback, but now my main purpose in the future is to fill in the lack of inventory information. When the project is finally successfully completed, its final state is quite different from the original assumption, which also affects the change of shrinkage rate. Moreover, this change is gradually evolving with their thinking and decision-making path

3. It is necessary to include external data

it projects usually focus on information within the enterprise: accounting, materials, inventory, sales orders, customer contracts, etc. However, in order to improve the decision-making level more effectively, analysis projects must collect information outside the enterprise. Since we may not be able to control or even know how to obtain the required external information, data collection must be included in the work

if the purpose of analysis projects is to improve the level of product life cycle management, it is not enough to rely solely on internal information. Although internal information (sales data, inventory records, product line strategies, pricing and discount information, success criteria, marketing results, etc.) does contribute to better product lifecycle management, external relevant data are also necessary: similar products, embedded advertising, macro economic conditions, weather, school schedule, etc. - all information that may affect the prospects of the product

once we know what external information can improve decision-making, the next step is to determine their importance and availability, and how much we are willing to pay for it

4. There are three concepts to guide my practice of analysis projects. First, such projects should be able to clearly improve the decision-making level; Secondly, the better decision-making comes from the accurate establishment of causality; Finally, it is very difficult and risky to establish an accurate causal relationship

why are there great risks? We often confuse relevance with causality, but high relevance does not mean the inevitable existence of causality

finding the right reason can obviously promote business intelligence, which is the basis for improving the decision-making level. How to do it? For beginners, we should carefully clarify the causal relationship. However, before identifying and applying these relationships, we must verify whether they exist. For example, can reducing the waiting time in the call center promote sales? Can lengthening the test cycle improve product quality? Try to verify these first

analytical projects also need to be trusted by it: major projects can be delivered; It can change its traditional role to create business value through analysis; Know how to improve the quality of analysis; Be able to use multiple methods to complete Bi projects. (end)

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