Data analysis in business analytics. TIBCO Spotfire: Big Data Analytics

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. Olga Gorchinskaya, Director of Research Projects and Head of Big Data Force, told CNews about expertise in this area, large-scale implementations, proprietary solutions, and the world's largest Oracle solutions testing center.

15.10.2015

Olga Gorchinskaya

In recent years, a generation of leaders has changed. New people came to the management of 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 for solving 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. Midsize and small businesses alike understand the benefits of moving from Excel to dedicated analytics solutions.

If we compare this situation with the one that was in the companies even five years ago, we will see significant progress. In recent years, a generation of leaders has changed. New people came to the management of 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 for solving 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 complex general economic and political situation plays a role. It is holding back 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 a 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. There are not many organizations nowadays that do not use business analysis. And, apparently, the time of active growth in the implementation of large corporate analytical systems is passing.

And, finally, it is important to note that now the customers are shifting their emphasis in the use of BI-tools, which is holding back the growth of the number of projects we are used to. The fact is that the leading vendors - Oracle, IBM, SAP - base 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 advantages, this leads to a great dependence of business users on IT specialists: if it is necessary to include some new data in the scope of consideration, the business has to constantly turn to IT to load data, harmonize it with existing structures, include it in the general model, etc. etc. Now we see that business wants more freedom, and for the sake of being able to add new structures on their own, interpret and analyze them at their own discretion, users are ready to sacrifice some part of corporate consistency.

So now the focus is on lightweight tools that allow end users to work directly with the data without worrying much about corporate consistency. As a result, we are seeing the successful advancement of Tableaux and Qlick, which allow it to work in the Data Discovery style, and some loss of 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, tools that we previously thought were too lightweight for the enterprise level are taking the lead. 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 kind of utopia, it is not so popular and even limits the promotion of analytical technologies, since in practice every department, or even an individual user, wants independence and freedom. There is nothing wrong with that. After all, in the same bank, risk professionals 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, tools that we previously thought were too lightweight for the enterprise level are taking the lead. These are solutions of the Data Discovery class. They are based on the idea of simplicity of working with data, speed, flexibility and an easy-to-understand presentation of the 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 "vague" meaning and not always clear value. In this case, more flexible tools are required than classic business analysis tools.

"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 the equipment and software testing processes as accessible as possible. It is a kind of data center for partner testing systems and cloud solutions.

CNews: How Big Data Technologies Help Develop Business Intelligence?

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 existed before Big Data. Now interest in machine learning, statistics is increasing, and with the help of these big data technologies, it is possible to expand the functionality of a traditional business system focused on computing and visualization.

In addition, the concept of data warehouses has been expanded by the use of Hadoop technology, which has led to new standards for building corporate storage in the form of data lakes.

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

Olga Gorchinskaya:

We use big data technologies in BI projects in several cases. The first is when it is necessary to improve the performance of an existing data warehouse, which is very important in an environment when companies are rapidly increasing the amount of information they use. 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 Hadoop tooling, which is very efficient due to its very architecture, flexible, adaptable to specific needs and profitable from an economic point of view, 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, we were talking about large volumes of regularly arriving data with a changing structure. This information must be processed, disassembled, extracted from it numerical indicators, as well as to save the original data. Considering the significant increase in the volume of incoming information, using relational storage for this became too expensive and ineffective way. We have created a separate Hadoop cluster for processing primary documents, the results of which are loaded into the relational storage for analysis and further use.

The second direction is the introduction of in-depth analytics tools to expand the functionality of the BI-system. This is a very promising area, since it is associated not only with solving IT problems, but also with creating new business opportunities.

Instead of organizing special projects to implement in-depth analytics, we try to expand the scope of existing projects. For example, forecasting indicators based on available historical data is a useful function for almost any system. 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 analysts who meet these requirements. They carried out a project in the field of healthcare for the formation of regulatory reporting, and in addition, within the framework of this project, forecasting of the workload of medical organizations and their segmentation by statistical indicators was implemented. The value of such forecasts for the customer is clear, 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 city management in a similar way.

And finally, we have experience in implementing big data technologies where it comes to 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 appraisal system for ROSEKO, commissioned by the Russian Society of Appraisers. To select analog objects, the system collected data from sources on the Internet, processed this information using linguistic technologies and enriched it using geo-analytics using machine learning methods.

CNews: What own solutions "Force" is 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 for enriching customer knowledge. It can be used in various industries: 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 for enriching customer knowledge.

A typical use case is developing targeted marketing campaigns. If the company has 20 million customers, it is unrealistic to distribute all advertisements in the database. It is necessary to narrow the circle of ad recipients, and the objective function here is to increase customer response to the marketing proposal. In this case, we can upload basic data about all clients (names, surnames, dates of birth, place of residence) into ForSMedia, and then, based on information from social networks, add new useful information to them, including circle of interests, social status, family composition, area of professional activities, music 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 media is a very rich source, although it is difficult to work with. 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 on the basis of big data technologies and allows enriching customer data and analyzing results on a massive scale. Technologies used include Hadoop, R statistical research framework, RCO linguistic processing tools, Data Discovery tools.

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

The use of innovative integrated Oracle complexes 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 not cheap, but due to the scale of the tasks being solved, they fully justify themselves.

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

Olga Gorchinskaya:

In this direction, we do not just provide test stands, 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 the equipment and software testing processes as accessible as possible. The idea did not arise out of nowhere. For almost 25 years, Force has been developing and implementing solutions based on Oracle technologies and platforms. 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 to master these systems, called it ExaStudio. On its basis, dozens of companies could discover the possibilities of new Exadata software and hardware 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 presents a full line of the latest Oracle hardware and software systems - from Exadata and Exalogic to the Big Data Appliance big data machine - which, in fact, act as test benches for our partners and customers. In addition to testing, here you can get services for auditing information systems, migrating to a new platform, setting up, configuring and scaling.

The center is actively developing in the direction of using cloud technologies. Not so long ago, the architecture of the Center was refined in such a way as to provide its computing resources and services in the cloud. Customers can now take advantage of the self-service performance capabilities of uploading test data, applications, and testing to the cloud.

As a result, a partner company or a customer can, without preliminary investments 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 on the transition 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.

Every large business and most medium-sized structures are faced with the problem of providing management with inaccurate data on the state of affairs of the company. The reasons may be different, but the consequences are always the same - wrong or untimely decisions that negatively affect the effectiveness of financial transactions. To exclude such situations, a professional business analytics system or BI ( from English - Business Intelligence). These high-tech "assistants" contribute to the construction of a system of management control of every aspect of 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 cut. As a result, management gets quick access to complete and transparent information about the state of affairs of the company. A feature of reports obtained with the help of BI is the ability of the manager to independently choose in which context to receive information.

Modern Business Intelligence systems are multifunctional. That is why in large companies they are gradually replacing other methods of obtaining business reports. Experts refer to their main capabilities:

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 distribution of data;
Transparent work with data;
Providing a clear connection between information from various sources;
Flexible and intuitive setting of employee access rights 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 a manager not to depend on the IT department or his assistants to provide the required information. It is also a great opportunity to demonstrate the correct direction of your decisions, not in words, but in precise 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 and others. The above corporations spend a lot of money on business intelligence, but the implemented BI systems bring invaluable benefits.

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

Visibility. The main interface of any business analysis software should reflect the main indicators. Thanks to this, the manager will quickly be able to assess the state of affairs in the enterprise and begin to take something if necessary;
Customization. Each user should be able to customize the interface and function keys in the most convenient way for themselves;
Layering. Each dataset should have several sections (layers) to provide the level of detail that is needed at a particular level;
Interactivity. Users should be able to collect information from all sources and from several directions at the same time. It is necessary that the system has the function of configuring the notification by key parameters;
Multithreading and access control. In the BI system, the simultaneous operation of a large number of users should be implemented with the ability to set them different levels of access.

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

When thinking about the implementation of BI-class systems, it is important to remember that the success of the project will largely depend on the attitude of the company's employees to innovation. This applies to all IT products: skepticism and fear of downsizing can undermine all implementation efforts. Therefore, it is very important to understand what feelings the business intelligence system evokes in future users. The ideal situation will arise when the company's employees treat the system as an assistant and a tool for improving their work.

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

Goals and objectives of implementing BI systems;
Requirements for storing data and the ability to operate with them;
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;
Visualization capabilities. For each person, the ideal BI analytics looks different, and the system must meet the needs of each user;
Versatility or narrow specialization. There are systems in the world aimed at a specific industry, as well as universal solutions that allow you to collect information in any aspect;
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 the management make an informed choice among all the variety of well-known business intelligence systems. There are other parameters (eg storage structure, web architecture), but these require expertise in narrow IT areas.

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

Correctness of data. If the data for the analysis is incorrect, then there is a possibility of a serious system error;
Comprehensive training for each user;
Fast implementation. You need to focus on getting the right reports right at all key locations, rather than serving a single user perfectly. You can always adjust the appearance of the report or add another section of it for convenience after implementation;
Realize the ROI on 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 implementation of the BI system 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 years of the existence of business analysis software, experts have formulated 9 key mistakes that can reduce efficiency to a minimum:

Non-obviousness of the purpose of implementation for management. Often, a project is created by the IT department without the close involvement of managers. In most cases, in the process of implementation and operation, questions arise about the purpose and objectives of the BI system, the benefits and usability;
Lack of transparency in management, work of employees and decision-making. Managers may not know the algorithms for the work of field employees, and management decisions can 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;
Insufficient data reliability. Falling false information into the business analysis system is unacceptable, otherwise employees will not be able to trust it and use it;
The 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 choice, speak for themselves. As a result, a system is 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;
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 regarding improvements;
Transfer of the BI system to a third-party organization for support. As practice shows, most often such situations lead to product isolation and 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;
Desire to save money. In business, this is normal, but BI analytics only works if it takes into account all aspects of the company's activities. This is why high-value deep analytics systems are most effective. The desire to receive several reports on areas of interest leads to frequent data errors and a large dependence on the qualifications of IT specialists;
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;
Lack of a unified business analysis strategy at the enterprise. Without a single course chosen for all employees, any BI class system will be just a set of disparate reports that satisfy the requirements of individual managers.

The implementation of BI systems is an important step that can help bring 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 a project for implementing a business analysis system.

Accessible work with Big Data using visual analytics

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

The Spotfire interface makes it equally convenient to work with both small datasets and multi-terabyte clusters of big data: sensor readings, information from social networks, points of sale or geolocation sources. Users of all skill levels can easily navigate meaningful dashboards and analytic workflows simply by using visualizations that graphically represent the aggregation of billions of data points.

Predictive analytics is learning on the job based on the company's shared experience to make more informed decisions. Using Spotfire Predictive Analytics, you can discover new market trends from business intelligence insights and take action to minimize risk, leading to better management decisions.

Overview

Big Data Connectivity for High-Performance Analytics

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

On-Demand Analytics Data Visualization: Built-in, user-configurable data connectors that facilitate ultra-fast, interactive data visualization
Analysis in a database (In-Database Analytics): integration with a distribution computing platform that allows you to make calculations of data of any complexity based on big data.
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 enables business users to access, aggregate, and analyze data from any data source through powerful, easy-to-use dashboards and workflows.

Big data connectors

Spotfire big data connectors support all kinds of data access: in-datasource, in-memory and on-demand. Spotfire's built-in data connectors include:

Hadoop Certified 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 sensors

In-Datasource Distributed Computing

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

Users work with dashboards with visual selection functionality that access scripts using the built-in capabilities of the TERR language,
TERR scripts initiate distributed computing functionality in interaction with Map / Reduce, H2O, SparkR, or Fuzzy Logix,
These applications in turn access highly efficient systems like Hadoop or other data sources,
TERR can be deployed as an advanced analytics engine in Hadoop nodes that are driven by MapReduce or Spark. TERR 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-grade statistics package that was developed by TIBCO for full R compatibility, leveraging the company's many years of analytical system experience associated with S +. This allows customers to continue to develop applications and models not only using open source R, but also integrate and deploy their R code on a commercial, reliable platform without having to rewrite their code. TERR has higher efficiency and reliable memory management, provides higher data processing speed on large volumes compared to the open source R language.

Combining all the functionality

Combining the aforementioned powerful functionality means that even for the most complex tasks requiring highly reliable analytics, users interact with simple, easy-to-use interactive workflows. This allows business users to visualize and analyze data, as well as share analytics results, without the need to know the details of the data architecture underlying the business analysis.

Example: Spotfire interface for configuring, running and visualizing the results of a model that defines the characteristics of lost loads. Through this interface, business users can perform computations using TERR and H2O (a distributed computing framework) by accessing 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, create 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 expand 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, Gradient Boosting Machine (GBM)
Generalized linear (additive) models ( Generalized Additive Models)
Neural networks

Content analysis

Spotfire provides analytics and data visualization, much of which was not previously used - it is unstructured text that is stored in sources such as documents, reports, CRM system notes, site logs, publications on social networks 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 enables sophisticated analytics to be used while working with maps. In addition to geographic maps, the system creates maps to visualize the behavior of users, warehouses, production, raw materials and many other indicators.

Business intelligence, or BI, is a general term that means a variety of software products and applications created to analyze the primary data of an organization.

Business analysis as an activity consists of several related processes:

data mining (data mining),
analytical processing in real time (online analytical processing),
retrieving information from databases (querying),
making report (reporting).

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

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, which would otherwise be based on intuition and chance.

Although BI systems are promising enough, their implementation can be difficult due to 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 (such as Hardee’s, Wendy’s, Ruby Tuesday, and T.G.I. Friday’s) are actively using business intelligence systems. BI is extremely useful for them to make strategic decisions. What new products to add to the menu, what dishes to exclude, what inefficient points to close, etc. They also use BI for tactical issues such as renegotiating contracts with product suppliers and identifying ways to improve ineffective processes. Because restaurant chains are strongly focused on their internal business processes and because BI is central to controlling these processes, helping to manage enterprises, restaurants, among all industries, are part of an 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 uses data analysis and cluster analysis extensively to maintain its dominant position in the sector. Harrah’s has changed the fundamentals of its competitive gaming policy, focusing on customer loyalty and service level analysis rather than maintaining a mega casino. Amazon and Yahoo are not just large web projects, they are heavily using business intelligence and a common test-and-understand approach to streamline their business processes. Capital One conducts over 30,000 experiments annually to identify target audiences and evaluate credit card offerings.

Where or from whom should BI implementation start?

Overall employee engagement is vital to the success of BI projects because 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 salespeople. Their primary responsibility is to drive 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 they receive with its help.

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 significant differences in the performance of several departments, they try to bring the "lagging" departments to the level at which the "leading" ones work.

Once you've implemented business intelligence across your sales teams, you can continue to deploy it in other departments in your organization. A positive salesperson experience will drive other employees' technology transitions.

How to implement a BI system?

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

Good BI systems must provide users with context. It is not enough just to make reports on what the sales were like 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, it won't pay off for BI implementation if users feel “threatened” or skeptical about the technology and, as a result, refuse to use it. BI, being deployed for “strategic” purposes, should, in theory, fundamentally change the way the company operates and the decision-making process, so IT leaders need to be careful about the opinions and reactions of users.

7 stages of launching BI systems

Make sure your data is correct (reliable and suitable for analysis).
Conduct comprehensive user training.
Implement the product as quickly as possible, getting used to using it already during implementation. It is not worth spending a lot of time developing “perfect” reports, as reports can be added as the system evolves and user needs. Create reports that will provide maximum value quickly (users are at the highest level for these reports) and then adjust them.
Take an integrative approach to building your data warehouse. Make sure you don't lock yourself into a data strategy that doesn't work in the long run.
Before you start, be clear about your ROI. Identify the specific benefits you intend to achieve, and then check them against the actual results every quarter or every six months.
Focus on your business goals.
Don't buy analytics software because you think that you need it. Implement BI with the idea that there are metrics among your data that need to be obtained. At the same time, it is important to have at least a rough idea of where exactly they can be.

What problems can arise?

The biggest obstacle to the success of BI systems is user resistance. Other potential problems include sifting through large amounts of irrelevant information and poor quality data.

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

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

The third obstacle in transforming business processes using a BI system is the lack of understanding by companies of their own business processes. As a consequence, companies simply do not understand how these processes can be improved. If the process does not have a direct impact on profit or the company is not going to standardize processes in all its departments, the implementation of a BI system may be ineffective. Companies need to understand all the activities and all the functions that make up a separate 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 that 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 of the benefits of using BI solutions

A large number of BI applications have helped companies more than recoup their investment. Business intelligence systems are used to explore ways to reduce costs, identify new business opportunities, visualize ERP data, and quickly respond to changing demand and optimize prices.

In addition to increasing the availability of data, BI can provide companies with more negotiation benefits by making it easier to assess supplier and customer relationships.

Within an enterprise, there are many opportunities to save money by streamlining business processes and overall decision-making. BI can effectively help improve these processes by shedding light on the failures they have made. For example, employees at a company in Albuquerque used BI to identify ways to reduce mobile phone use, overtime and other recurring costs, saving the organization $ 2 million over three years. Also, with the help of BI solutions, Toyota realized that it had twice overpaid its carriers for 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 operational management decisions.
Pay attention to data quality.
Think about the performance metric that matters most to the business.
Provide context that influences your performance metric.

And remember, BI is about more than decision support. Through advances in technology and the way IT leaders implement them, BI systems have the potential to transform organizations. CIOs who successfully use BI to improve business processes are making a much more meaningful contribution to their organization, as leaders who implement basic reporting tools.

Based on materials from www.cio.com

(Business Intelligence).

Young specialists who are making a successful career as analysts in high-tech companies such as Microsoft, IBM, Google, Yandex, MTS, etc. are invited to the seminar as speakers. At each seminar, students are told about some of the business problems that are solved in these companies, about how the accumulation of data occurs, how the tasks of data analysis arise, what methods can be used to solve them.

All invited specialists are open for contacts, and students will be able to contact them for advice.

Objectives of the workshop:

contribute to bridging the existing gap between university research and the solution of practical problems in the field of data analysis;
facilitate the exchange of experience between present and future professionals.

The seminar is held regularly at the Faculty of CMC MSU on Fridays at 18:20 , lecture hall P5(first floor).

Seminar attendance is free(if you do not have a pass to Moscow State University, please inform the organizers of the seminar in advance to submit the list of participants for the shift).

Workshop program

date	Speaker and workshop topic
September 10, 2010 18:20	Alexander Efimov , Head of the Analytical Department of the MTS Retail Network. Predicting the effect of marketing campaigns and optimizing store assortment. Application page: Optimization of the assortment of outlets (data problem).
September 17, 2010 18:20	Vadim Strizhov , Researcher at the Computing Center of the Russian Academy of Sciences. Bank Credit Scoring: Methods for Automatically Generating and Selecting Models. The classic and new technologies for constructing scoring cards are considered. The seminar explains how customer data works and how to generate the most plausible scoring model that meets, moreover, the requirements of international banking standards.
September 24, 2010 18:20	Vladimir Krekoten , Head of Marketing and Sales, Otkritie brokerage house. Applying mathematical methods to predict and counter customer churn. Practical problems arising in the analysis of the customer base in marketing are considered. The tasks of clustering and segmentation of customers, scoring of new customers, tracking the dynamics of target segments are set. Application page: Clustering clients of a brokerage company (data problem).
October 1, 2010 18:20	Nikolay Filipenkov , and about. Head of the Credit Scoring Department of the Bank of Moscow. Applying Mathematical Methods to Manage Retail Credit Risk. Some practical aspects of building scoring models and risk assessment are considered. Application page: Retail Credit Risk Management (Data Problem).
October 8, 2010 18:20	Fedor Romanenko , Manager of the Search Quality Department, Yandex. History and principles of web search ranking. The article deals with the use and development of Information Retrieval methods, from text and link ranking to Machine Learning to Rank in the problem of Internet search. The fundamental principles behind modern web ranking are set out in relation to search engine success stories. Emphasis is placed on the impact of search quality on market performance and the vital need to continually improve search quality.
October 15, 2010 18:20	Vitaly Goldstein , developer, Yandex. Geographic information services Yandex. It tells about the Yandex.Traffic jams project and other Yandex geoinformation projects, about where the initial data for building geoinformation systems come from, about a new scalable data processing technology, about the competition of Internet mathematics and some promising problems. Data is provided and a formal statement of the problem of road map restoration is given. Application page: Building a road graph based on vehicle track data (data problem).
October 22, 2010	The workshop was canceled.
October 29, 2010 18:20	Fedor Krasnov , Vice President for Business Processes and Information Technology, AKADO. How do I get customer data?