BI and big data

Demand for Business Intelligence software will grow. A.T. Kearney predicts that worldwide spending on Big Data IT systems will increase 30% this year, achieving an entire market size of $114B.

The predictive and advanced analytics software market will worth $3.4 billion this year, reaching a 9.9% growth rate from 2013. Simplified tools deliver intuitive user interfaces and simple features for the relevant business adoption.

Business intelligence makes use of services and software to transform data into meaningful insights that help to make data-driven decisions. BI tools reach and analyze data sets and show analytical findings in graphical reports, charts and maps.

Business intelligence helps to track changes and capture valuable data. BI tools work on “data warehouses” to eliminate unimportant information and take advantage of key data.

Big Data vs Business Intelligence

Big Data is aimed to handle a huge amount of data. Usually, the main purpose is identifying the main questions to make a difference. These questions serve for further reporting and exploration. Moreover, big data helps to involve analytics into the business processes.

These are Big Data functions:

  • Gather and store big amounts of data efficiently.
  • Dissect the data so that the company may capture a great understanding of customer expectations and what needs to be improved.
  • Collect the data and maintain the analysis directly. This is especially important in a secure fashion that deals with privacy regulations.
  • Decrease the delays and latency.

Big data became a buzzword now: many people talk about it but not all of them know exactly what it is. However, it has changed our everyday life. Big data made us smarter and helped to speed up processes by structuring the data. So, now it’s easier to make use of it. Big data offers new valuable insights for the better decisions.

Business Intelligence is the collecting of software, systems, and products, which may import big data streams and apply them to create meaningful data for business use.

BI involves deep analysis of raw data, and then companies present information in an easy-to-consume way based on authentic real-time facts.

Related article: What Is a Smart Contract: Making Sense of the Noise

big data vs business intelligence

What BI and Big Data Can and Can’t Do

Abilities of big data.

  1. Predictive analysis. It helps to focus company’s efforts on the specific areas that require leaders attention the most. Moreover, it allows detecting fraud. For example, big data may refine pattern detection and eliminate criminal behavior.

BI and big data help optimizing marketing campaigns. For instance, a company can understand better consumer behavior and boost cross-sell opportunities.

Many organizations use predictive analysis to plan resources and inventory. For instance, airlines apply predictive analytics to assign prices.

Banks use predictive analytics to decrease risks. For example, credit scores are applied to evaluate buyer’s likelihood of default for buy.

  1. Diagnostic analysis. This type of analytics helps to identify the reasons of something. For instance, in SMM, it means usage of descriptive analytics to evaluate the number of reviews, pins, posts, mentions, followers, page views, etc. It helps to build a successful strategy.

It is helpful when your goal is recognizing the tendency and trends. Diagnostic analysis is backward looking, dependent/target variable with independent, and pays attention to the causal relationships and sequences. Also, it includes a relative ranking of dimensions/variable.

  1. Descriptive analytics or data mining helps to enlighten patterns that can bring insight. Descriptive analytics is helpful in the sales cycle to classify clients by their preferences. Also, descriptive analytics may be used for evaluating credit risk and using data about previous financial performance to forecast the next steps.

Data mining for business intelligence works this way: it captures data, analyzes it from various perspectives or dimensions, and creates a summary. Technically, data mining requires searching correlations in big relational databases.

These are key features of it:

  • Capture, transform and upload data as a part of centralized data management.
  • Keep and handle the data in a database system.
  • Visualize the data by the graphs or tables.
  • Dissect the data by application software.
  • But most importantly, deliver data access to the companies.

Actually, this is a meeting point between scientists and businessmen. It helps to save money and achieve greater goals. Nowadays, data-mining tools are the essential part of risk management and making decisions.

Do you know the potential of data mining for business intelligence?

Data mining tools ensure better CRM and help to take advantage of all valuable company’s data. Business intelligence helps to build an entire strategy of the company. Moreover, it delivers the valuable information to address existing business problems: financial control, cost optimization, entering new markets, etc.

Data mining makes decision making more accurate and faster, helps to refine internal business processes, enhance operational efficiency and brings new revenues. It is important for reaching competitive advantages, recognize market trends and address business challenges.

Data mining and Business Intelligence are must-have for healthcare and financial organizations, sales and marketing departments because they deliver fast analysis and refine problem-solving processes.

  1. Prescriptive analysis. It helps to predict a problem and find out a way to solve it. This is a useful tool for the front-line workers. For instance, it may be used to identify the personalized approach for a customer to improve customer satisfaction. For example, Wine.com applies Bain & Company’s technology to motivate chatting with wine experts to refine customer loyalty. Besides, prescriptive analytics uses industrial-scale data analysis.

Prescriptive analysis keeps growing and gaining automated solutions where it replaces manual work. For instance, automated analytics will apply applications to select relevant marketing email The prescriptive analytics market is increasing and experts predict a growth of 22 percent between 2014 and 2019 to $1.1 billion. Moreover, it will be a part of business analytics software by 2020.

business intelligence best practices

What Big Data Cannot Do

  1. Guarantee that predictions will become a reality. Big data helps to deliver more than 90% of accuracy by applying strong machine learning tools. But it doesn’t mean that everything that you forecast will come true. There are many factors that can influence the future, and if these factors will change, results will be different as we expect.
  2. Imputation of the new data source. Imputation is very time-consuming. Moreover, it requires business understanding and creativity.
  3. Generate creative solutions. The only human brain can develop unusual and really creative decisions to the specific problems. Sometimes, it requires a good level of communication with some people to clarify things and set needed changes. Keep in mind that AI is coded by human and creativity is hard to gain through algorithms.
  4. Search solution to a vague problem. It is difficult to form an analytics problem based on a business problem. Moreover, this is quite challenging to describe a problem and find out key reasons, especially for the abstract problems.
  5. Data management. Data is increasing each day, and sometimes it includes unusual information. For example, graph data fits network analysis but it is worthless for campaign data. Such kind of data requires human attention and can’t be handled by the machine.

Business Intelligence Best Practices

Let’s dive deeper to identify best practices for appropriate BI tools.

#1 Simplify.

Make your tools easy to use for the wide audience with various background. For instance, Jabil allows their users to achieve more information.

But how to achieve this?

  • Automate the analysis phase and cut the time required for the users to make the analysis. So, they can develop and embed changes into the process.
  • Recognize needed actions with specific parameters.
  • Launch a subscription model to deliver required information to your target audience.

#2 Use business people for the development.

Business people know the key demands and processes of commerce. They may create some templates for the further development. It is important to create products with the end goals in mind. This method also helps to reach simplicity and pay more attention to the results.

#3 Work directly with the customers.

When you hang out or just briefly communicate with the customers, you know better their needs and expectations. Apply an “incubation” method with prototypes to test ideas. It helps to fail early and improve your strategy. Feel free to engage the users to test the tools.

#4 Create a centralized database of high-quality data.

Accurate data is the important factor. So, capture it from external and internal resources. After that, exceptions have to be recognized and fixed. It helps to refine data quality and decrease the number of mistakes.

#5 Reach critical mass.

Developing the database architecture helps to maintain scalability and create a foundation for BI tools.

Companies want to reach the right audience with the right data. Moreover, they want to engage users to assess processes. Analytic database platforms are made to maintain advanced analytics and data discovery. Appropriate data visualization, analytics, dashboard-based information, and SaaS offerings motivate users to participate in business processes.

#6 Predict new revenue streams.

It is important to forecast a number of new users and income that they can bring. To achieve this goal, managers use statistics of vertical growth rates, social media, fixed and variable infrastructure costs.

Best Practices in Mobile Business Intelligence

BI security.

Many users worry about the privacy of data that can be handled by the mobile device management software. So, plan ahead your strategy with a close look and special treatment of security issues. It will help to improve user engagement and satisfaction.

It’s all about people.

Identify what employees need to get access to mobile BI, and then expand it to the wider audience. Moreover, determine devices and standards that are required as well.

Engage your users not only to consume data. They appreciate new experiences and you can help them. For example, they may upgrade databases for more accurate information.

Managers may apply analytics to explore patterns based on various sources of data, allowing them to explore data relationships and provide data-driven decisions about how to deal with existing challenges.

Analytics and mobile BI may bring service, sales, and maintenance of the staff who permanently interact with the clients. So, managers will be well equipped with information about their purchase habits. Moreover, clients want customer support managers and sales personnel to know in advance their wishes, struggles, etc.

Mobile BI design.

The main goal is to create a design and BI tools purpose-built. Developers need to know exactly how to achieve the same level of user satisfaction on different devices.

So, feel free to explore our business intelligence best practices to make use of new technologies. It is important nowadays in order to ensure accurate results and understand better your customers. It is almost impossible to handle a huge amount of raw data for the managers, and easy for big data systems.

Advanced analytics has a power to predict the potential outcome, customer behavior and preferences. Yes, it doesn’t guarantee 100% accuracy but up to 90% of the business forecast can be true.

Moreover, big data and business intelligence help to prevent fraud and ensure needed level of security. A centralized base of high-quality data allows refine the quality of the company’s operations and earn more with fewer efforts.