Goals of data analysis. Business Information Analysis - Basic Principles Business Intelligence Data Analysis Approaches

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Affordable work with Big Data using visual analytics

Improve business intelligence and solve routine tasks using the information hidden in Big Data using the TIBCO Spotfire platform. It is the only platform that provides business users with an intuitive, user-friendly user interface that allows them to use the full range of Big Data analytics technologies without the need for IT professionals or special education.

The Spotfire interface makes it equally convenient to work with both small data sets and multi-terabyte clusters of big data: sensor readings, information from social networks, points of sale or geolocation sources. Users of all skill levels easily access rich dashboards and analytical workflows simply by using visualizations, which are graphical representations of the aggregation of billions of data points.

Predictive analytics is learning by doing based on shared company experience to make better informed decisions. Using Spotfire Predictive Analytics, you can discover new market trends from your business intelligence insights and take action to mitigate risk to improve management decisions.

Review

Connecting to Big Data for High-Performance Analytics

Spotfire offers three main types of analytics with seamless integration with Hadoop and other large data sources:

1. Data visualization on demand (On-Demand Analytics): built-in, user-configurable data connectors that simplify super-fast, interactive data visualization
2. Analysis in the database (In-Database Analytics): integration with the distributed computing platform, which allows you to make data calculations of any complexity based on big data.
3. In-Memory Analytics: Integration with a statistical analysis platform that pulls data directly from any data source, including traditional and new data sources.

Together, these integration methods represent a powerful combination of visual exploration and advanced analytics.
It allows business users to access, combine and analyze data from any data source with powerful, easy-to-use dashboards and workflows.

Big data connectors

Spotfire Big Data Connectors support all types of data access: In-datasource, In-memory and On-demand. Built-in Spotfire data connectors include:

• Certified Hadoop Data Connectors for Apache Hive, Apache Spark SQL, Cloudera Hive, Cloudera Impala, Databricks Cloud, Hortonworks, MapR Drill and Pivotal HAWQ
• Other certified big data connectors include Teradata, Teradata Aster and Netezza
• Connectors for historical and current data from sources such as OSI PI touch sensors

In-datasource distributed computing

In addition to Spotfire's handy visual selection of operations for SQL queries that access data distributed across data sources, Spotfire can create statistical and machine learning algorithms that operate within data sources and return only the results needed to create visualizations in the Spotfire system.

• Users work with dashboards with visual selection functionality that access scripts using the built-in features of the TERR language,
• TERR scripts invoke distributed computing functionality in conjunction with Map/Reduce, H2O, SparkR, or Fuzzy Logix,
• These applications in turn access high performance systems like Hadoop or other data sources.
• TERR can be deployed as an advanced analytics engine on Hadoop nodes that are managed with MapReduce or Spark. The TERR language can also be used for Teradata data nodes.
• The results are visualized on Spotfire.

TERR for advanced analytics

TIBCO Enterprise Runtime for R (TERR) – TERR is an enterprise-level statistical package that has been developed by TIBCO to be fully compatible with the R language, building on the company's years of experience in the S+-related analytics system. This allows customers to continue developing applications and models not only using open source R, but also to integrate and deploy their R code on a commercially secure platform without having to rewrite their code. TERR is more efficient, has better memory management, and provides faster data processing speeds over large volumes than the open source R language.

Combining all functionality

The combination of the aforementioned powerful functionality means that even for the most complex tasks that require high-level analytics, users interact with simple and easy-to-use interactive workflows. This allows business users to visualize and analyze data, and share analytics results, without having to know the details of the data architecture that underpins business intelligence.

Example: Spotfire interface for configuring, running and visualizing the results of a model that characterizes lost cargo. Through this interface, business users can perform calculations using TERR and H2O (a distributed computing framework) on transaction and shipment data stored in Hadoop clusters.

Analytical space for big data

Advanced and predictive analytics

Users use Spotfire's visual selection dashboards to launch a rich set of advanced features that make it easy to make predictions, build models, and optimize them on the fly. Using big data, analysis can be done inside the data source (In-Datasource), returning only the aggregated information and results needed to create visualizations on the Spotfire platform.

Machine learning

A wide range of machine learning tools are available in Spotfire's list of built-in features that can be used with a single click. Statisticians have access to the program code written in the R language and can extend the functionality used. Machine learning functionality can be shared with other users for easy reuse.

The following machine learning methods are available for continuous categorical variables on Spotfire and on TERR:

• Linear and logistic regression
• Decision trees, Random forest algorithm, Gradient boosting machines (GBM)
• Generalized linear (additive) models ( Generalized Additive Models)
• Neural networks

Content analysis

Spotfire provides analytics and data visualization, much of which has not been used before - this is unstructured text that is stored in sources such as documents, reports, CRM system notes, site logs, social media posts and much more.

Location analytics

High resolution layered maps are a great way to visualize big data. Spotfire's rich map functionality allows you to create maps with as many reference and functional layers as you need. Spotfire also gives you the ability to use sophisticated analytics while working with maps. In addition to geographical maps, the system creates maps to visualize user behavior, warehouses, production, raw materials and many other indicators.

Every large business and most medium-sized structures face the problem of providing management with inaccurate data on the state of the company's affairs. The reasons may be different, but the consequences are always the same - wrong or untimely decisions that adversely affect the effectiveness of financial transactions. To avoid such situations, a professional business intelligence or BI system is designed ( from English. – business intelligence). These high-tech "assistants" contribute to building a system of managerial control of every aspect within the business.

At its core, BI systems are advanced analytical software for business analysis and reporting. These programs can use data from various sources of information and provide them in a convenient form and section. As a result, management gets quick access to complete and transparent information about the state of affairs of the company. A feature of the reports obtained with the help of BI is the ability of the manager to independently choose in which context to obtain information.

Modern Business Intelligence systems are multifunctional. That is why in large companies they are gradually crowding out other ways of obtaining business reporting. Their main capabilities include:

• Connections to various databases, in particular, to;
• Formation of reports of varying complexity, structure, type and layout at high speed. It is also possible to set a schedule for generating reports on a schedule without direct participation and data distribution;
• Transparent work with data;
• Ensuring a clear link between information from different sources;
• Flexible and intuitive configuration of access rights for employees in the system;
• Saving data in any format convenient for you - PDF, Excel, HTML and many others.

The capabilities of business intelligence information systems allow the manager not to depend on the IT department or his assistants to submit the required information. It is also a great opportunity to demonstrate the right direction of your decisions not with words, but with exact numbers. Many large network corporations in the West have been using BI systems for a long time, including the world-famous Amazon, Yahoo, Wall-Mart, etc. The above-mentioned corporations spend decent money on business intelligence, but the implemented BI systems bring invaluable benefits.

The benefits of professional business intelligence systems are based on principles that are supported in all advanced BI applications:

1. visibility. The main interface of any business analysis software should reflect key metrics. Thanks to this, the manager will quickly be able to assess the state of affairs in the enterprise and begin to do something if necessary;
2. Customization. Each user should be able to customize the interface and function keys in the most convenient way for themselves;
3. Layering. Each data set should have several cuts (layers) to provide the detail of information that is needed at a particular level;
4. Interactivity. Users should be able to collect information from all sources and in several directions at the same time. It is necessary that the system has the function of setting alerts by key parameters;
5. Multithreading and access control. The BI system should be able to implement simultaneous work of a large number of users with the ability to set different access levels for them.

The entire IT community agrees that business intelligence information systems are one of the most promising areas for the development of the industry. However, their implementation is often hampered by technical and psychological barriers, uncoordinated work of managers and the lack of prescribed areas of responsibility.

When considering the implementation of class BI systems, it is important to remember that the success of the project will largely depend on the attitude of the company's employees to the innovation. This applies to all IT products: skepticism and fear of downsizing can thwart all implementation efforts. Therefore, it is very important to understand how the business intelligence system makes future users feel. The ideal situation will be when the company's employees will treat the system as an assistant and a tool for improving work.

Before starting a project to introduce BI technology, it is necessary to conduct a thorough analysis of the company's business processes and the principles of managerial decision-making. After all, it is these data that will be involved in the analysis of the situation in the company. It will also help to make a choice of a BI system along with other main criteria:

1. Goals and objectives of the implementation of BI systems;
2. Requirements for data storage and the ability to operate with them;
3. Data integration functions. Without using data from all sources in the company, management will not be able to get a holistic picture of the state of affairs;
4. Visualization capabilities. For each person, the ideal BI analytics looks different, and the system must meet the needs of each user;
5. Universality or narrow specialization. In the world, there are systems aimed at a specific industry, as well as universal solutions that allow you to collect information in any context;
6. Demanding resources and the price of a software product. The choice of a BI system, like any software, depends on the capabilities of the company.

The above criteria will help management make an informed choice among the variety of known business intelligence systems. There are other parameters (for example, data storage structure, web architecture), but they require skills in narrow IT areas.

It is not enough just to make a choice, buy software, install and configure it. Successful implementation of BI systems of any direction is based on the following rules:

• Data correctness. If the data for analysis is incorrect, then there is a possibility of a serious system error;
• Full training for each user;
• Rapid implementation. It is necessary to focus on the correct formation of the necessary reports in all key places, and not on the ideal service for one user. You can always adjust the appearance of the report or add another section for convenience after implementation;
• Understand the return on investment in your BI system. The effect depends on many factors and in some cases is visible only after a few months;
• The equipment should be designed not only for the current situation, but also for the near future;
• Understand why the BI implementation was started, and do not demand the impossible from the software.

According to statistics, only 30% of company executives are satisfied with the implementation of BI systems. Over the long years of the existence of business analysis software, experts have formulated 9 key mistakes that can reduce efficiency to a minimum:

1. Non-obviousness of the purpose of implementation for management. Often the project is created by the IT department without the close participation of managers. In most cases, in the process of implementation and operation, questions arise regarding the purpose and objectives of the BI system, the benefits and ease of use;
2. Lack of transparency in management, work of employees and decision-making. Managers may not know how employees work in the field, and management decisions may be made not only on the basis of dry facts. This will lead to the impossibility of maintaining the existing paradigm as a result of the implementation of the BI system. And it is often impossible to break the culture of corporate governance that has developed over the years;
3. Insufficient reliability of data. It is unacceptable for false information to enter the business analysis system, otherwise employees will not be able to trust it and use it;
4. Wrong choice of a professional business intelligence system. Many examples in history when management hires a third-party organization to implement a BI system and does not take part in its selection speak for themselves. As a result, a system is being introduced that does not allow obtaining the required report or with which it is impossible to integrate one of the existing software in the company;
5. Lack of a plan for the future. The peculiarity of BI systems is that it is not static software. It is impossible to finish an implementation project and not think about it. There are many requirements from users and management in terms of improvements;
6. Transfer of the BI system to a third-party organization for support. As practice shows, most often such situations lead to the isolation of the product and the isolation of the system from the real state of affairs. Own support service responds much faster and more efficiently to user feedback and management requirements;
7. Desire to save. In business, this is normal, but BI analytics only works if it takes into account all aspects of the company's activities. That is why deep analytical systems with high cost are the most effective. The desire to receive several reports on areas of interest leads to frequent errors in the data and a great dependence on the qualifications of IT specialists;
8. Different terminology in the company. It is important that all users understand the basic terms and their meaning. A simple misunderstanding can lead to misinterpretation of the reports and indicators of the BI system;
9. Lack of a unified strategy for business analysis in the enterprise. Without a single course selected for all employees, any BI class system will be just a set of disparate reports that meet the requirements of individual managers.

Implementing BI systems is an important step that can help take your business to the next level. But this will require not only a fairly large infusion of finance, but also the time and effort of each employee of the company. Not every business is ready to competently complete the project of implementing a business analysis system.

Over the decades of working with large customers, Force has accumulated vast experience in the field of business analysis and is now actively developing big data technologies. In an interview with CNews, Olga Gorchinskaya, Director of Research Projects and Head of Big Data at Force, spoke about expertise in this area, large-scale implementations, proprietary solutions, and the world's largest Oracle solution testing center in an interview with CNews.

15.10.2015

Olga Gorchinskaya

In recent years, the generation of leaders has changed. New people came to manage companies, who made their careers already in the era of informatization, and they are used to using computers, the Internet and mobile devices both in everyday life and to solve work problems.

CNews: To what extent are BI tools in demand by Russian companies? Are there any changes in the approach to business analysis: from "analytics in the style of Excel" to the use of analytical tools by top managers?

Olga Gorchinskaya:

Today, the need for business analysis tools is already quite high. They are used by large organizations in almost all sectors of the economy. Both SMBs and SMBs are also realizing the benefits of moving from Excel to dedicated analytics solutions.

If we compare this situation with the one that was in companies five years ago, we will see significant progress. In recent years, the generation of leaders has changed. New people came to manage companies, who made their careers already in the era of informatization, and they are used to using computers, the Internet and mobile devices both in everyday life and to solve work problems.

CNews: But there are no more projects?

Olga Gorchinskaya:

Recently, we have noted a slight decrease in the number of new large BI projects. First, the difficult general economic and political situation plays a role. It hinders the start of some projects related to the introduction of Western systems. Interest in solutions based on free software also delays the start of BI projects, since it requires preliminary study of this software segment. Many Open Source analytics solutions are not mature enough to be widely used.

Secondly, there has already been a certain saturation of the market. Now there are not so many organizations where business analysis is not used. And, apparently, the time of active growth of implementations of large corporate analytical systems is passing.

And, finally, it is important to note that customers are now shifting their focus in the use of BI tools, which is holding back the growth in the number of projects we are used to. The fact is that the leading vendors - Oracle, IBM, SAP - build their BI solutions on the idea of ​​a single consistent logical data model, which means that before analyzing something, it is necessary to clearly define and agree on all concepts and indicators.

Together with the obvious benefits, this leads to a high dependence of business users on IT specialists: if it is necessary to include some new data in the circle of consideration, the business has to constantly turn to IT to download data, align it with existing structures, include it in a common model, etc. d. Now we see that businesses want more freedom, and for the sake of being able to independently add new structures, interpret and analyze them at their own discretion, users are willing to sacrifice some part of corporate consistency.

Therefore, lightweight tools are now coming to the fore, allowing end users to work directly with data and not care much about corporate-level consistency. As a result, we are seeing the successful promotion of Tableaux and Qlick, which allow you to work in the style of Data Discovery, and some loss of the market by large solution providers.

CNews: This explains why a number of organizations are implementing several BI systems - this is especially noticeable in the financial sector. But can such informatization be considered normal?

Olga Gorchinskaya

Today, the leading role is played by tools that we used to consider too lightweight for the enterprise level. These are solutions of the Data Discovery class.

Olga Gorchinskaya:

Indeed, in practice, large organizations often use not a single, but several independent analytical systems, each with its own BI tools. The idea of ​​a corporate-wide analytical model turned out to be a bit of a utopia, it is not so popular and even limits the promotion of analytical technologies, since in practice every department, and even an individual user, wants independence and freedom. There is nothing terrible in this. Indeed, in the same bank, risk specialists and marketers need completely different BI tools. Therefore, it is quite normal when a company chooses not a cumbersome single solution for all tasks, but several small systems that are most suitable for individual departments.

Today, the leading role is played by tools that we used to consider too lightweight for the enterprise level. These are solutions of the Data Discovery class. They are based on the idea of ​​ease of working with data, speed, flexibility and easy-to-understand presentation of analysis results. There is another reason for the growing popularity of such tools: companies are increasingly experiencing the need to work with information of a changing structure, generally unstructured, with a "blurred" meaning and not always clear value. In this case, more flexible tools than classical business analysis tools are in demand.

Force has created the largest in Europe and unique in Russia platform - Fors Solution Center. Its main task is to bring the latest Oracle technologies closer to the end customer, to help partners in their development and application, to make hardware and software testing processes as accessible as possible. This is a kind of data center for partners to test systems and cloud solutions.

CNews: How do big data technologies help business analytics develop?

Olga Gorchinskaya:

These areas - big data and business intelligence - are moving closer to each other and, in my opinion, the line between them is already blurred. For example, deep analytics is considered "big data" even though it has existed since before Big Data. Now the interest in machine learning, statistics is increasing, and with the help of these big data technologies, it is possible to extend the functionality of the traditional business system focused on calculations and visualization.

In addition, the concept of data warehouses was expanded by the use of Hadoop technology, which led to new standards for building corporate storage in the form of a “data lake” (data lakes).

CNews: What are the most promising tasks for big data solutions?

Olga Gorchinskaya:

We use big data technologies in BI projects in several cases. The first is when it is necessary to increase the performance of an existing data warehouse, which is very important in an environment where companies are rapidly growing the amount of information used. Storing raw data in traditional relational databases is very expensive and requires more and more processing power. In such cases, it makes more sense to use the Hadoop toolkit, which is very efficient due to its very architecture, flexible, adaptable to specific needs and economically beneficial, since it is based on an Open Source solution.

With the help of Hadoop, we, in particular, solved the problem of storing and processing unstructured data in one large Russian bank. In this case, it was about large volumes of regularly incoming data of a changing structure. This information must be processed, parsed, extracted from it numerical indicators, as well as to save the original data. Given the significant growth in the volume of incoming information, using relational storage for this became too expensive and inefficient. We have created a separate Hadoop cluster for processing primary documents, the results of which are loaded into a relational storage for analysis and further use.

The second direction is the introduction of advanced analytics tools to expand the functionality of the BI system. This is a very promising direction, since it is associated not only with the solution of IT problems, but also with the creation of new business opportunities.

Instead of organizing special projects to implement advanced analytics, we are trying to expand the scope of existing projects. For example, for almost any system, a useful function is to predict indicators based on available historical data. This is not such an easy task, it requires not only skills in working with tools, but also a certain mathematical background, knowledge of statistics and econometrics.

Our company has a dedicated team of data scientists who meet these requirements. They completed a project in the field of health care on the formation of regulatory reporting, and additionally, within the framework of this project, forecasting the workload of medical organizations and their segmentation by statistical indicators was implemented. The value of such forecasts for the customer is understandable, for him it is not just the use of some new exotic technology, but a completely natural expansion of analytical capabilities. As a result, interest in the development of the system is stimulated, and for us - new work. We are now implementing predictive analytics technologies in a project for urban management in a similar way.

And, finally, we have experience in implementing big data technologies where we are talking about the use of unstructured data, primarily various text documents. The Internet offers great opportunities with its huge volumes of unstructured information containing useful information for business. We had a very interesting experience with the development of a real estate valuation system for the ROSEKO company commissioned by the Russian Society of Appraisers. To select analogous objects, the system collected data from sources on the Internet, processed this information using linguistic technologies and enriched it with the help of geo-analytics using machine learning methods.

CNews: What own solutions is Force developing in the areas of business intelligence and big data?

Olga Gorchinskaya:

We have developed and are developing a special solution in the field of big data - ForSMedia. It is a social media data analysis platform to enrich customer knowledge. It can be used in various industries: the financial sector, telecom, retail - wherever they want to know as much as possible about their customers.

Olga Gorchinskaya

We have developed and are developing a special solution in the field of big data - ForSMedia. It is a social media data analysis platform to enrich customer knowledge.

A typical use case is the development of targeted marketing campaigns. If a company has 20 million customers, it is unrealistic to distribute all advertisements in the database. It is necessary to narrow the circle of recipients of ads, and the objective function here is to increase the response of customers to a marketing offer. In this case, we can upload basic data about all clients to ForSMedia (names, surnames, dates of birth, place of residence), and then, based on the information from social networks, supplement them with new useful information, including circle of interests, social status, family composition, professional area. activities, musical preferences, etc. Of course, such knowledge can not be found for all clients, since a certain part of them do not use social networks at all, but for targeted marketing, such an “incomplete” result gives huge advantages.

Social networks are a very rich source, although it is difficult to work with it. It is not so easy to identify a person among users - people often use different forms of their names, do not indicate age, preferences, it is not easy to find out the characteristics of a user based on his posts, subscription groups.

The ForSMedia platform solves all these problems based on big data technologies and allows you to enrich customer data in bulk and analyze the results. Among the technologies used are Hadoop, the R statistical research environment, RCO's linguistic processing tools, and Data Discovery tools.

The ForSMedia platform makes maximum use of free software and can be installed on any hardware platform that meets the requirements of a business task. But for large implementations and with increased performance requirements, we offer a special version optimized for operation on Oracle hardware and software systems - Oracle Big Data Appliance and Oracle Exalytics.

The use of innovative integrated Oracle systems in large projects is an important area of ​​our activity not only in the field of analytical systems. Such projects will turn out to be expensive, but due to the scale of the tasks being solved, they fully justify themselves.

CNews: Can customers somehow test these systems before making a purchase decision? Do you provide, for example, test benches?

Olga Gorchinskaya:

In this direction, we do not just provide test benches, but have created the largest in Europe and unique in Russia platform - Fors Solution Center. Its main task is to bring the latest Oracle technologies closer to the end customer, to help partners in their development and application, to make hardware and software testing processes as accessible as possible. The idea didn't come out of nowhere. Force has been developing and implementing solutions based on Oracle technologies and platforms for almost 25 years. We have extensive experience working with both clients and partners. In fact, Force is the Oracle competence center in Russia.

Based on this experience, in 2011, when the first versions of the Oracle Exadata database engine appeared, we created the first laboratory for the development of these systems, calling it ExaStudio. On its basis, dozens of companies could discover the possibilities of new Exadata hardware and software solutions. Finally, in 2014, we turned it into a kind of data center for testing systems and cloud solutions - this is the Fors Solution Center.

Now our Center has a full line of the latest Oracle software and hardware systems - from Exadata and Exalogic to the Big Data Appliance - which, in fact, act as test benches for our partners and clients. In addition to testing, here you can get services for auditing information systems, migration to a new platform, customization, configuration and scaling.

The center is also actively developing towards the use of cloud technologies. Not so long ago, the architecture of the Center was finalized in such a way as to provide its computing resources and services in the cloud. Now customers can take advantage of the productive capacity of the self-service scheme: upload test data, applications to the cloud environment and perform testing.

As a result, a partner company or customer can, without prior investment in equipment and pilot projects on their territory, upload their own applications to our cloud, test, compare performance results and make one or another decision to switch to a new platform.

CNews: And the last question - what will you present at Oracle Day?

Olga Gorchinskaya:

Oracle Day is the main event of the year in Russia for the corporation and all its partners. Force has repeatedly been its general sponsor, and this year too. The forum will be entirely devoted to cloud topics - PaaS, SaaS, IaaS, and will be held as Oracle Cloud Day, since Oracle pays great attention to these technologies.

At the event, we will present our ForSMedia platform, as well as talk about the experience of using big data technologies and projects in the field of business intelligence. And, of course, we will tell you about the new capabilities of our Fors Solution Center in the field of building cloud solutions.

Business intelligence, or BI, is a general term that refers to a variety of software products and applications designed to analyze an organization's primary data.

Business analysis as an activity consists of several interconnected processes:

• data mining (data mining),
• real-time analytical processing (online analytical processing),
• getting information from databases (querying),
• making report (reporting).

Companies are using BI to make informed decisions, cut costs and find new business opportunities. BI is something more than ordinary corporate reporting or a set of tools for obtaining information from enterprise accounting systems. CIOs use business intelligence to identify underperforming business processes that are ripe for redesign.

Using modern business analysis tools, businessmen can start analyzing data on their own and not wait for the IT department to generate complex and confusing reports. This democratization of access to information enables users to back up their business decisions with real numbers that would otherwise be based on intuition and chance.

Despite the fact that BI systems are quite promising, their implementation can be hampered by technical and "cultural" problems. Managers need to provide clear and consistent data to BI applications so that users can trust them.

Which companies use BI systems?

Restaurant chains (for example, Hardee's, Wendy's, Ruby Tuesday and T.G.I. Friday's) actively use business intelligence systems. BI is extremely useful to them for making strategically important decisions. What new products to add to the menu, what dishes to exclude, what inefficient outlets to close, etc. They also use BI for tactical issues such as renegotiating contracts with product suppliers and identifying ways to improve inefficient processes. Because restaurant chains are strongly focused on their internal business processes, and because BI is central to the control of these processes, helping to manage enterprises, restaurants, among all industries, are among the elite group of companies that really benefit from these systems.

Business intelligence is one of the key components of BI. This component is essential to the success of a company in any industry.

In the retail sector, Wal-Mart makes extensive use of data analysis and cluster analysis in order to maintain its dominant position in the sector. Harrah's has changed the fundamentals of its competitive gaming policy to focus on analyzing customer loyalty and service levels instead of maintaining a mega-casino. Amazon and Yahoo are not just big web projects, they are actively using business intelligence and a common “test and understand” approach to streamline their business processes. Capital One conducts more than 30,000 experiments annually to identify the target audience and evaluate credit card offers.

Where or with whom should the implementation of BI start?

Overall employee engagement is vital to the success of BI projects, as everyone involved in the process must have full access to information in order to be able to change the way they work. BI projects should start with top management and the next group of users should be sales managers. Their main responsibility is to increase sales, and wages often depend on how well they do it. Therefore, they will much more quickly accept any tool that can help them in their work, provided that this tool is easy to use and that they trust the information received with it.

You can order your pilot project on the business analysis platform.

Using BI systems, employees adjust work on individual and group tasks, which leads to more efficient work of sales teams. When sales leaders see a significant difference in the performance of several departments, they try to bring the "lagging" departments to the level at which the "leading" ones are performing.

Having implemented business intelligence in sales departments, you can continue to implement it in other departments of the organization. A positive salesperson experience will encourage other employees to adopt new technologies.

How to implement a BI system?

Before implementing a BI system, companies should analyze the mechanisms for making managerial decisions and understand what information managers need to make these decisions more informed and faster. It is also desirable to analyze in what form managers prefer to receive information (as reports, graphs, online, in paper form). Refinement of these processes will show what information the company needs to receive, analyze and consolidate in its BI systems.

Good BI systems should provide users with context. It is not enough to simply report what sales were yesterday and what they were a year ago on the same day. The system should make it possible to understand what factors led to exactly this value of sales on one day and another - on the same day a year ago.

Like many IT projects, BI adoption will not pay off if users feel “threatened” or skeptical about the technology and stop using it as a result. BI, when implemented for "strategic" purposes, is supposed to fundamentally change how a company functions and makes decisions, so IT leaders need to pay special attention to the opinions and reactions of users.

7 stages of launching BI systems

1. Make sure that your data is correct (reliable and suitable for analysis).
2. Provide comprehensive user training.
3. Implement the product as quickly as possible, getting used to using it already in the course of implementation. You don't have to spend a huge amount of time developing "perfect" reports, because reports can be added as the system evolves and users need it. Build reports that deliver the most value quickly (user demand for these reports is the highest) and then tweak them.
4. Take an integrative approach to building a data warehouse. Make sure you don't lock yourself into a data strategy that doesn't work in the long run.
5. Before you start, clearly estimate the ROI. Determine the specific benefits you intend to achieve and then test them against actual results every quarter or every six months.
6. Focus on your business goals.
7. Don't buy analytics software because you think that you need it. Implement BI with the idea that there are indicators among your data that you need to get. At the same time, it is important to have at least a rough idea of ​​where exactly they can be.

What problems might arise?

A major obstacle to the success of BI systems is user resistance. Other possible problems include the need to "sift through" large amounts of irrelevant information, as well as data of poor quality.

The key to getting meaningful results from BI systems is standardized data. Data is a fundamental component of any BI system. Companies need to get their data warehouses in order before they can start extracting the information they need and trust the results. Without data standardization, there is a risk of getting incorrect results.

Another problem may be an incorrect understanding of the role of the analytical system. BI tools have become more flexible and user-friendly, but their main role is still reporting. Do not expect automated business process management from them. However, certain changes in this direction are still planned.

The third obstacle in the transformation of business processes using the BI system is the lack of understanding by companies of their own business processes. As a result, companies simply do not understand how these processes can be improved. If the process does not have a direct impact on profits, or the company does not intend to standardize processes in all its divisions, the implementation of a BI system may not be effective. Companies need to understand all the activities and all the functions that make up a single business process. It is also important to know how information and data is transferred through several different processes, and how data is transferred between business users, and how people use this data to carry out their tasks within a particular process. If the goal is to optimize the work of employees, all this must be understood before starting a BI project.

Some benefits of using BI solutions

A large number of BI applications have helped companies recoup their investments. Business intelligence systems are used to explore ways to reduce costs, identify new business opportunities, present ERP data in a visual form, and quickly respond to changing demand and optimize prices.

In addition to making data more accessible, BI can provide companies with more value during negotiations by making it easier to evaluate relationships with suppliers and customers.

Within an enterprise, there are many opportunities to save money by optimizing business processes and overall decision-making. BI can effectively help improve these processes by shedding light on the mistakes made in them. For example, employees at a company in Albuquerque used BI to identify ways to reduce cell phone usage, overtime, and other operating costs, saving the organization $2 million over three years. Also, with the help of BI solutions, Toyota realized that it overpaid its carriers by a total of $812,000 in 2000. Using BI systems to detect defects in business processes puts the company in a better position, giving a competitive advantage over companies that use BI is just to keep track of what's going on.

• Analyze how leaders make decisions.
• Think about what information managers need to optimize their operational decision-making.
• Pay attention to data quality.
• Think about the performance metric that matters most to your business.
• Provide context that influences the performance measure.

And remember, BI is about more than decision support. With advances in technology and how IT leaders implement it, business intelligence systems have the potential to transform organizations. CIOs who successfully use BI to improve business processes make a much more meaningful contribution to their organization, executives who implement basic reporting tools.

Sourced from www.cio.com