While many people do not know what BI (business intelligence) is the time, in fact, BI has done the whole link in the related work.
What specifically do BI? Popular point appreciated that the data access, data preparation, data analysis, data visualization series of operations to the data distribution applications.
These actions are just the process, the real purpose is to find out by the final results of the data obtained to improve business decisions.
Internet education platform as an example, each enterprise will be equipped with similar sales support operations or type of job, go to the registration site and statistical analysis of APP, active for the first time pay, pay once again, VIP, silence, loss of other data.
The data generated by each platform export into an Excel integration process can be understood as a data access ; and the weight to these data, the process can be understood as a clear simple data preparation ; conversion rate calculated by the function of each hopper understood as analysis data ; data analysis results by visual presentation charts called data visualization ; visualization and graph theme to PPT in turn considered to report data distribution application .
So the question is, data access is not a repeat of export and import must do the work? Source data from multiple systems and how to integrate the different structures? In addition to the data to see what leadership PPT?
Imagine when you are well-prepared for a week of sales data reporting, and the boss suddenly asked why an abnormal data, you do have to re-do the analysis for this report abnormal data? How long will the boss waiting to take it?
When increasing the amount of data companies, data analysis requirements of increasingly deep dimensions getting smaller, and even real-time and interactive put forward higher requirements. But this time, a lot of things that can not be resolved labor reporting, BI can be resolved. BI value not tell you how much conversion funnel students, but to tell you why that number, where you can improve.
BI history from the tool to the "decision-making brain" of evolution
The concept of BI (business intelligence) was first proposed by the Gartner Group in 1996, and in fact IBM researcher Hans Peter Luhn as early as 1958 to use the concept. He will be "smart" is defined as "an ability to understand the relationship between things, and to rely on this ability to guide decision-making, in order to achieve the desired goal."
Accountability to the application level, BI in fact, has gone through four stages of development:
Excel Report: birth at this stage of a professional group is "cousin cousin", they have to export large amounts of data from different companies, ERP, CRM, financial systems every day, and then associate multiple tables calculated using vlookup and sumif, Finally, the chart visualization screenshot into the PPT conducted daily and weekly reports. As for leadership in the end will not look, they do not care, because of inability to care.
Reporting system: is an upgraded version of traditional reporting, and data sources can be docked directly to a business system, the response speed of data than the Excel improved significantly, already support rights management, etc., but still biased in favor of data reporting, decision support is difficult.
Traditional BI: First, the data source can dock multiple systems to integrate all the data into a single platform in the global analysis. The second is to support real-time data display, analysis and depth dimensions is far stronger than the reporting system to support interactive drill data, linkage. Finally, the data carrier having an amount of reaction speed and obvious advantages, not only reporting tools, can be better decision support.
Smart BI: and as is traditional BI decision-support, but stressed the low level of user codes (or zero code) develop, seamless, flexible deployment, such as with a view of distant Smart ETL Torah dragged into billboards can do analysis without re modeling, enabling ordinary business people the ability to do data analysis, so that data members more time to focus on how to combine analysis and business. And, also we can build a future based on the ability to model the aid of AI algorithms, such as sales forecasting, intelligent row of class and so on.
From role-playing point of view, BI development can be understood as "decision-making brain" of evolution from the data analysis tools to the depth of the scene. Most companies just want to start doing the work efficiency of data analysis through its increase, and to the back, more companies aim is to improve the efficiency of decision making and scientific, results-oriented.
When the coexistence of four products, enterprises how to choose
From Excel to present cutting-edge intelligent BI, BI evolution has been following the market demand and change. Of course, the development of big data technology, cloud computing, artificial intelligence, but also to the development of BI to create more possibilities.
Commercial development of the whole society forward, but each time, each industry has different levels of development companies, which is why evolution is so BI progress, and the reason these types of data analysis products can still coexist.
Currently, more controversial subject in the choice of a reporting system, traditional BI and intelligent BI.
Select the data analysis software for business purposes must first be clear what you want the introduction of related systems Yes. If the amount of data that enterprises in general, data analysis is just to give each department presented the final report on the results do not need help decision-makers at all levels of decision making, then the reporting system to meet basic needs. But you have to consider when companies increasing amount of data, found that the market reaction speed when the competitors have a lot ahead of his time, we are not even going to select BI, now that there is no need to step in place.
Naturally, if the decision is energized, have had enough before to see the data will always be delayed one week always want to know the reason can not find the answer in a meeting, or an enterprise data itself ahead of consciousness, of course, is to choose BI . Furthermore, if the butt is to simplify the development process and the amount of BI, data analyst to reduce the daily high code, highly repetitive work, we would recommend more intelligent BI.
The intelligent BI can also be understood as "AI + BI", on behalf of the BI development trend in the next five years, as well as many other industry leading data analysis and service providers in the field of joint exploration. AI first companies to do the project have to have enough data based on a very clear project objectives, and have long-term planning, we recommend to implement. Second, be sure to choose a company with a big data analysis AI gene. Outlook data is currently far and nearly ten retail giant customer demand forecasting cooperation, with replenishment intelligent, smart scheduling, marketing plans and other cooperation scene.
BI scenarios in all walks of life
With the advance of information technology, each firm has accumulated a massive data base, and for enterprises, but it is double-edged sword. The more the amount of data, the greater the value can be obtained, however, if there is no powerful data analysis capabilities, huge amounts of data will become an obstacle to efficient business decisions. In this case, BI naturally become the era of big data companies to improve their competitiveness nuclear weapons.
Head of big data social electricity supplier on behalf of the little red book once said: growth too fast is also a worry, in the phased approximation exponential growth curve, means that large data operations will face more challenges, have only 60 times data capabilities in order to support twice the amount of users, 30 times the amount of data grows.
BI has its own data analysis scenarios in each area. In the consumer retail sector, outlook far summarizes the data contain merchandise, stores, marketing, channels, supply chain, customer relations, finance, human resources, etc., the eight business scenarios.
In addition to retail + Internet industry, manufacturing, e-commerce, financial, medical and other industries also have a corresponding scene analysis.
(Distant view data products demo- Internet education)
(Distant view data products demo- field of electronic business)
Internet education: can be transformed into the funnel conversion rate for the channel, to analyze the popularity of different courses, parental feedback and ratings, and lecturer.
E-commerce: analyzes the scene also create customer value as the core of users, marketing, merchandise, traffic, warehousing, distribution, customer service and other integration.
Manufacturing: the analysis about the enterprise procurement, production, sales, distribution, inventory each scene.
Insurance: can do damages and insurance costs analysis, customer analysis, risk analysis, product analysis.
Financial Securities: can be analyzed for financial products, customer returns, credit management, customer water and other scenes.
The pharmaceutical industry: can be analyzed around the drug operations, supply chain, finance, marketing, electricity supplier channels, such as business scene
Car market: by processing and analyzing vast amounts of information on the vehicle data, road data, context-aware data, auto service providers to fine the owners of the management, one-stop car service program.
Enterprises according to their own needs, find the right way, step by step to build intelligent data analysis indicator system integration.
BI in the future there will be what new trends
Over the past few years, BI has experienced a shift from the tool to the role of "decision-making brain", and the future will be the "Intelligent Decision brain" transformation.
View of far-founder and CEO Su Spring Garden data representation, BI's future must be AI + BI.
The next five years, BI will not only stay in a multidimensional statistics of historical data. By fusion with the growing popularity of algorithms and calculation power, it will achieve a more automatic, more intelligent data exploration, real-time early warning, forecasting the future, automated diagnosis and recommendations for action. On experience, it will certainly become increasingly "fool", emphasizing agility, ease of use and a scene of industry, and continues to integrate access to a richer, more fine-grained data source, further extension of data-driven decision-making scenarios.
Future every enterprise needs to build a data-based decision-making brain, cut from BI, AI technology continues to upgrade, see 3 years, 3 months to do, is a road map can be rational landing.