In the future of Business Intelligence BI, how do you view the ai+bi model?

I was chatting with a friend online yesterday , Asked a question , What do you think AI（ Artificial intelligence ）+BI（ business intelligence ） This pattern and direction , Let me give you my personal opinion .

With me in business intelligence BI Industries and enterprises involved in the project , From the very traditional information-based enterprises with relatively weak foundation , To enterprises with a solid foundation of informatization, such as financial and banking industries , We haven't seen it yet   Artificial intelligence AI+ business intelligence BI  It has a very wide range of applications .

AI+BI The status quo of

Let's start with business intelligence BI, namely Business Intelligence. This business intelligence BI The concept has been around for decades , I have been working for so long , When a customer asks about business intelligence BI This intelligence of  Intelligence  How exactly is it reflected , Can you find a scene to demonstrate its intelligence . Tell the truth , I can't answer , Because I haven't studied it . I answered jokingly , This problem is the same as buying wife cakes , I bought a cake without giving it to my wife .

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therefore , business intelligence BI This intelligence has not understood , Now there is artificial intelligence AI, namely Artificial Intelligence, Another intelligence ,AI+BI These two intelligences add up , Should be more intelligent ,Double  intelligence , But we still haven't seen the real 、 Can definitely stand the challenge 、 Actual business scenarios for large-scale applications .

AI+BI Development history of

A few years ago, Microsoft's business intelligence BI product  Power BI  stay Ignite  New features announced at the Technical Conference , Automatic generation of visual charts through natural language queries , Later, it took a step further to replace natural language query through voice conversion , This is business intelligence BI+AI A preliminary attempt at .

Data visualization  - Parker data business intelligence BI Visual analysis platform

It was a little early , actually IBM Of Watson Analytics It has realized the natural language query to generate visual charts . And there is a scenario that I think is more valuable, which is the analysis of driving factors , I remember that the case analysis was about the comparison of training spending trends of various departments , For example, the training expenses of the sales department 、 Training expenses of the finance department . Next to this visualization, a very valuable recommendation analysis or insight is given  Discovery, What insight has been gained ？ Insight is ： What drives the high cost of training , The final conclusion is , Position  Position  And department organization  Organization  These two factors .

Next, we will show how different positions and department organizations affect the data analysis results of high training costs . I think this analysis scenario is  AI + BI  A good scene , It's worth studying . What it actually restores is , First of all , Show results . second , Reveal what factors affect the level of this achievement . Third , Why does it affect , Show the results of detailed analysis .

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In the process , business intelligence BI In fact, we can only achieve the first step , Position the problem through visual analysis . But what causes this problem , Driven by what factors , Drive details , The second and third steps require business data analysts to be involved in business intelligence BI Of . But because of  IBM Watson Analytics  The algorithm engine behind this product is in the cloud , Deployed abroad , At the same time, it can not support Chinese naturallanguageprocessing , Therefore, it cannot be used in China .

Of course , Is the result of its analysis necessarily correct ？ In fact, it depends on the integrity of the data we provide , The more detailed the data 、 The more complete the data 、 The higher the quality of data cleaning , The accuracy of this analysis can be guaranteed . But the reality is , Many of our enterprises have incomplete data at the source 、 Data quality and many other problems , It is doomed that the value of this kind of analysis will be very low .

last year 2020 year Gartner  On business intelligence BI The magic quadrant of analysis  Leaders  A new product appears in quadrant  ThoughtSpot, Before is  Power BI、Tableau、Qlik, This product incorporates a lot of AI The concept of , In fact, it's what I mentioned above  Power BI、IBM Watson Analytics  These contents of . therefore , There are also some business intelligence in China BI Products are moving in this direction , From the perspective of capital market , The capital market also likes to make such comparison , Namely  Gartner  In the Magic Quadrant , Domestic business intelligence BI Does the product have .

Data visualization  - Parker data business intelligence BI Visual analysis platform

actually , We do not deny AI+BI  The value and significance of technology itself , There is such an exploration on the whole business intelligence BI The development of the industry is not a bad thing . But what we have been thinking about is how to truly implement these concepts , Instead of letting companies pay for these concepts , Use the last discovery  AI + BI  Actually, I did   business intelligence BI  Live , Or say  AI + BI  The application value brought to the enterprise has not far exceeded   business intelligence BI  Bring more tangible value to the enterprise .

AI+BI The landing direction of

I have also thought about it seriously , In my submission  AI + BI  In the fall to the ground , It should have these characteristics and directions ：

First of all , Must have occurred in  IT  Industries with relatively solid informatization foundation , At the same time, business intelligence BI It has been applied to a deeper industry . So this is the way to think about it , Industries like finance and Banking 、 The e-commerce industry has these conditions , These industries have the highest degree of informatization 、 The level of data utilization is the highest , The business scenarios are rich and stable , business intelligence BI  Responsible for exhibition ,AI  Solve the problem of how to do after the results of the analysis come out , Previous manual decisions , To intelligent decision making . For example, risk control and marketing .

second , Certain combination is a segmentation field of deep scenario business , And these subdivisions are not universal , It means that each segment has its own unique AI+BI Solution . All of this analysis has been completed , All kinds of data are also very perfect , Various analytical models 、 Business scenarios have been tempered thousands of times , It's time to pass AI Automatically complete some algorithm matching , Drive the business execution according to the matching results . Like the retail industry 、 Marketing level , I think it still has AI Implementation of some scenarios , Just how competent these scenes are , This is something to explore and think about .

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therefore ,AI+BI  This model is the future  BI  A direction of development . business intelligence BI  On the underlying data architecture, it solves the problem of big data 、 Data Lake 、 Docking of data midrange , This is an extension of the basic data architecture . business intelligence BI  The upward extension is a variety of analytical applications , Then to intelligent analysis application , And finally return to the process of business execution , Finally, a complete closed loop is formed .

But at present, for most traditional enterprises , The challenge is how to make business intelligence BI Give good questions . Cultivate data awareness in this process 、 Business data mindset 、 Data awareness 、 Awareness of data analysis , If none of these infrastructure construction processes 、 Are missing words , Is unable to master a higher level of technology application .