#businessanalytics

Artificial Intelligence for data analytics

Artificial Intelligence is widely used in many applications, including for data analytics. AI is used to analyze large data sets that allows to obtain valuable information, identify trends, make forecasts, etc. Machine learning algorithms provide fast and accurate work with huge data volumes.

It is important to use AI in the process of data analysis because there are several advantages:

Speed and efficiency: ensuring fast data processing (the user quickly receives accurate information as a result of analysis); fast decision-making process; the ability to remember commands and syntax of data analysis libraries, and suggest alternative approaches to analysis;
Verification and validation: detecting inconsistencies in data and determining the reasons for their occurrence;
Data democratization: providing democratized access to data (the ability of users without specific data knowledge to analyze large data sets and extract insights);
Automated reporting: automates the report generation process, ensuring timely access to information regardless of the location or origin of the data.

AI is supposed to complement the work of data scientists. The main ways to use AI in data analysis are:

Code generation and error correction when analyzing data (especially when working with large data sets);
Explanation of the analysis and conclusions for a deeper understanding of the situation (for example, why sales fell, reasons for the users increase, etc.);
Generating synthetic data (creating training data sets and entering them into machine learning models, which greatly simplifies the testing process);
Creating interactive dashboards and reports to quickly combine data from multiple sources in one place;
Automatic data entry from images (converting tabular data images into digital sets without manual work).

Data storage for quick insights

We know that the main and most valuable resource of modern business is data. However, is all data equally valuable?

Data loses its value within a few days – it becomes irrelevant and provides little benefit to the company. The goal of every company is to find a solution that can extract useful information and insights from data before it becomes outdated.

Data quality and algorithm efficiency can greatly complicate the challenge of delivering data in a timely manner. So, it is worth paying attention to the data warehouse. Making the right storage decisions is essential to delivering insights quickly.

At the very beginning of the digital revolution, companies stored data to comply with regulations or evaluate past performance. The purpose of data storage has changed over the past 10 years. Now historical data can help to make forecasts and determine future trends. This has led to the opening of new opportunities in the business decision-making process. At the same time, the volume of generated data has sharply increased, and many technologies have emerged for collecting and analyzing it.

Data storage is often not a priority. Huge amounts of data are still stored on disks. This significantly complicates access to data, increasing financial costs and energy consumption. A quality data storage solution frees up time and shifts the focus from high-cost issues to analytics, optimization, and AI solutions. For example, in pharmaceuticals, the time it takes to obtain information is essential in fighting pandemics, developing new, more effective drugs, and improving existing ones. Therefore, an efficient data storage solution is necessary for the operational process of data acquisition.

One of the common challenges in the data management process is data quality. Often the desire to put all the information into data lakes turned the lake into a «swamp». Data that does not correspond to reality reduces efficiency. Therefore, it is important for companies moving to data-driven management to emphasize how they measure and address quality gaps.

Data infrastructure can affect data quality. Built-in AI-based tools can ensure that information is stored correctly, taking into account requirements, mandatory checks and security measures. Data infrastructure presents enormous opportunity while eliminating the human costs and energy inefficiencies of legacy storage systems.

Key benefits of Business Analytics

An indispensable tool for modern business is Business Analytics. Regardless of the field of activity, each company generates huge data amounts. Proper work with it opens many opportunities for business development.

Business Analytics is a tool that uses quantitative methods to extract valuable information (meaning) from the data provided. Based on the information received, the business is able to make informed decisions, take certain actions and conduct a detailed analysis of the situation.

Key benefits of Business Analytics:

Making decisions. The main tool in the process of making informed and effective decisions is Business analytics. This tool can be used to make decisions in any business area.
Increasing efficiency. The use of BA contributes to the improvement of business operations: saving time, energy and resources. Employees can also make their suggestions by using Business Analytics to perform everyday tasks. And this information can be helpful in making decisions.
Budget maximization. A small business that doesn’t have big budgets can scale it up with BA. For example, to get more data about customers, to anticipate changes in their needs and preferences, to maintain a competitive position in the market.
Goals achievement. Using data visualization allows to monitor past and current performance against key performance indicators. Detailed charts help to see the full picture and, based on this, make certain decisions to achieve business goals.
Increasing income. The implementation of BA solutions leads to financial results. According to a McKinsey research, organizations that invest in big data increase profits by 6%.
Information receiving. With the help of Business Analytics tools, it is possible to analyze the target market, from opinions and actions. This will allow to review approaches, identify what works and what does not, and replace non-working ones with more effective solutions. This provides companies to meet the needs of customers, and therefore increase its overall efficiency.

The main skills to master Business Analytics

Success in any field depends on the level of possession of certain knowledge and skills. Business Intelligence is no exception and requires professionals with technical and non-technical skills, as well as experience and knowledge from various fields. For example, knowledge and understanding of business processes, basic SQL queries, skills in processing large amounts of data, skills in working with various programs, the ability to build diagrams, etc. Analytical skills, communication skills, technical skills, business knowledge is not a complete list of skills. Below is a list of the core skills you need to master in business analytics.

Statistics and Probability

One of the main technical requirements is knowledge of statistics and probability. This is because the purpose of business analytics is to find the answer in statistical data and probabilistic approaches. This will help to find relationships in the data and find the right solution. Also, statistics and probability allow to better understand the data and build more accurate forecasts.

Data visualization

Visualization is required to transform raw data into analytics-ready data. This tool allows business analysts to understand data and make strategic decisions to achieve goals and satisfy requests. To do this, they use scatter plots, time series sequences, polar area charts, timelines, line charts, and other visualization options.

Programming skills

Basically, business analysts deal with data and related code, which implies knowledge of a computer language. Programs such as Python make it possible to work with large amounts of data. Ability to work with database management systems will help to extract, generate and modify data from databases.

Negotiation skills

The link between the technical team and management is the business analyst team. The implementation of any business solution and vision involves negotiating multiple requirements with decision makers. A business analyst must be able to communicate and hear both parties and their conditions.

Communication skills

Another important skill is communication. The main task of a business analyst is to create a solution for business development. To do this, it is necessary to be able to clearly express your thoughts and points of view to the team members. Also, business analysts often attend meetings with clients and stakeholders, which also requires good communication skills.

Decision making

Offering solutions that will be beneficial for the business and its development is the task of a business analyst. The insights gained from the data can have a significant impact on the company’s decision-making process. This in turn will help to beat the competition and take the company to the next level. The ability to analyze a situation from different angles and predict likely outcomes helps business analysts make informed decisions.

Critical thinking

The critical thinking of business analysts helps to meet customer expectations by analyzing data based on various factors and business requirements. Also, business analysts must clearly understand the data they collect and the processes and procedures they use to obtain the data. Only high-quality data can guarantee an excellent result.

Problem Solving

Experience in problem solving helps in the future to solve new problems and challenges. The activity of a business analyst is quite complex at each stage, which implies the ability to solve problems from him. Understanding the source and cause of a problem speeds up the troubleshooting process.

Documentation

The business analyst must use documentation to present analysis and reports. This will allow business analysts to express their thoughts and decisions more easily and clearly in meetings with clients and other stakeholders. There are many simple documentation tools that business analysts can use in their work.

Excel

Excel is a popular tool for analytics and reporting. Business analysts use this tool for a variety of calculations, reporting, and exploring various metrics. This allows them to identify patterns. Excel spreadsheets also allow to calculate dimensions and metrics using formulas, allowing analysts to analyze, draw conclusions, and prepare documentation.

Business analytics role in Healthcare and Pharmaceuticals

Business intelligence is essential to the business, helping companies make informed and effective decisions. Regardless of the activity field of the company, business analytics is its integral component. In the healthcare and pharmaceutical industries, analytics is critical to maintaining a company’s competitive position in the market. Medical and pharmaceutical companies own a huge data amount that requires proper storage, processing and analysis. This, in turn, allows to see real information from patients, regulators, competitors, etc. The main business analytics goal in this area is to empower decision-making, develop and implement innovations designed to save lives.

How exactly business analytics helps pharmaceutical and healthcare companies:

Minimization of research and development costs

At the moment, there is a rapid increase in the cost of launching a new medicine. Also, patents on already existing and in-demand medicines are expiring. It becomes relevant to accelerate the process of new medicines development. In this case, analytics will allow to get the most out of large data sets, publications, scientific information, and also allow to create forecasts and make decisions;

Improving the design and quality of clinical trial results

The use of big data technology in the healthcare and pharmaceutical industries helps reduce costs and increase efficiency. This is possible due to the increased speed of clinical trials, analysis and determination of a large number of data points (historical data, patient monitoring data, demographic data, etc.). In turn, a qualitative study of the results of clinical trials provides an opportunity to improve the efficiency of diagnosing diseases;

Precision medicine

The modern world is made up of data. This leads to the complexity of their processing. Big data analytics helps solve this problem by combining data from various sources (medical records, medical sensors, genome sequencing, etc.). This allows to identify patterns and create medicines for the patient based on his individual needs;

Providing information to improve marketing strategy and sales

High-quality work with data contributes to a better market understanding, to analyze the work of sales representatives, to analyze marketing channels and make decisions based on this data. Healthcare and pharmaceutical data is growing exponentially. In this situation, it is important to have modern technologies for processing and analyzing data, as well as predicting future trends using historical trends and data.

Each company has its own requests and needs that need to be covered with the help of data. However, there are basic requirements for an effective result of working with data:

Data structuring

Increasing efficiency is possible with the help of the correct data organization, management and storage. In healthcare and pharmaceuticals data is used for any purpose: evaluating medicines, future use, market potential, funding clinical trials, etc. Data structuring provides a quality process for organizing, processing, extracting and storing data for effective work with them.

Data collection

Algorithms and modeling techniques help to identify data patterns and interrelation. This, in turn, allows to make more accurate forecasts in research, development, marketing, clinical trials, etc. The use of clustering, associative segmentation and data classification tools improves the quality of medicines development and delivery methods.

Artificial Intelligence and machine learning

These tools are used to manage a huge data amount. Pharmaceutical and healthcare companies are using AI and machine learning to find medicines more easily.

Visualization

Better perception and understanding of data, trends and patterns, information is possible with a graphical format. Data visualization helps analysts and clinicians identify patterns and interrelatoin and make informed decisions quickly.

Of course, working with data requires certain skills and training. At the moment, there are many online portals that offer tutorials. However, in order to choose a program, it is important to understand the data that the company owns, its needs and goals, as well as the tasks that employees must solve with the help of data.

Business analytics for startups

Relevant data is the main supporting element in achieving business success and market domination. Correct work with data and extracting the maximum benefit is provided by a business analytics.

Business analytics is a set of knowledge, technologies and practices that are used to explore data and company performance. Its main goal is to obtain the necessary information for making decisions based on data. Business analytics can be effective not only for large companies that have introduced it into all work processes, but also for startups. Many startups are successfully using business analytics to drive future success. Business analytics goal in this case is to identify useful data sets that will lead to increased productivity, efficiency, and revenue. Also, business analytics provides an opportunity to make an accurate forecast, anticipate future events related to the actions and behavior of consumers, market trends, create more efficient processes, etc. This tool is used to analyze data from various sources: cloud applications, CRM, social networks, etc.

How startups use business analytics to succeed:

Business analytics priority

A common mistake small companies make is to think that they don’t need analytics because of the small data amount. However, business analytics can be useful in the early stages. It is wise to start gathering data and developing an analyst strategy right away. In fact, startups own a lot of data (files, emails, calls, server data, third-party data, social media data, etc.). These data types are often overlooked, but it can greatly influence the decision-making process.

Data culture

Data is thinking. It is a cultural shift from making decisions based on intuition to making decisions based on data. A Harvard Business Review report found that 99% of CEOs are looking to transition to a data-driven culture. Creating a data culture in a company is a key factor in its success.

Right technology choosing

Almost all data is in the cloud, so the analytics platform should also be here. There are many vendors that offer analytics tools in the cloud. However, everyone has different capabilities. It is important to choose an analytical platform that takes into account the latest cloud technologies, which will meet all the requirements and requests of a particular company.

Artificial Intelligence

At the moment, Artificial Intelligence doesn’t require complex manipulations or a specialist team to introduce it into work processes. The latest business analytics platforms can automatically leverage the power of machine learning without the need for coding and high costs. With the help of the tool, text recognition, object detection in photos, sentiment analysis are possible.

Analytics are not just charts

Many users view business analytics only in the form of charts, graphs, and reports. However, modern business analytics platforms are much more than just visualization. It includes the entire data pipeline. Some platforms combine data collection and transformation with analytics, visualization, and machine learning. Such a complex solution is more efficient in the process of working with data.

All data collection

It is possible to get a complete picture in the case of analyzing all the data. The analytics platform must accept and process all data, including semi-structured and unstructured sources (NoSQL, files, emails, audio, video, social media etc.). This approach will help to make informed and effective data-driven decisions.

Infrastructure

It is important to consider the cost of the entire company infrastructure (database, data warehouse, servers, etc.). These elements can increase quite quickly, which in the end can be more expensive than the price of the platform. Cloud or serverless analytics platforms can start services and functions only when needed. This allows to save money by consolidating data warehouse into a single repository.

Business analytics features usage

There is no need to invest large sums at once. It makes sense to start with data collection and automation, and as a company grows, add visualization and advanced features.

Feedback

It is advisable to appoint a specialist in each company department who will provide feedback on data and analytics using. Each user should be able to use the data and conduct further analysis to complete their tasks and make decisions.

Self-service analytics

Self-service analytics provides users with tools and capabilities to solve various issues and quickly find the necessary information.

Business Intelligence vs Business Analytics

Business Intelligence and Business Analytics are the indispensable and most common tools of modern business to optimize workflows. To understand how each tool can benefit, let’s figure out the definitions.

Business Intelligence is the process of collecting, storing and examining data owned by a company to ensure smooth operations. BI includes various tools and software: spreadsheets, reporting system, monitoring software, data mining software, event management, online processing, dashboard development, etc. BI provides the ability to interpret big data, identify new opportunities and implement new development strategies. The ability to combine internal and external data is the BI advantage. This provides a clear current situation vision and understanding, that cannot be obtained using single data. Incorporating BI into the workflow helps organizations understand the business, market, customer needs and behavior.

Business Analytics is a statistical technology using quantitative methods for obtaining information, forecasting defects, and developing strategies. BA tools: predictive analysis, forecasting analysis, data aggregation, association and sequences identification, correlation analysis, factor analysis, data visualization, growth analysis, optimization, etc. BA usage involves the involvement of data processing specialists, as well as an additional specialized training. The decision-making process with BA occurs through numerical analysis methods (predictive modeling, analytical modeling, explanatory modeling).

What is the difference between Business Intelligence and Business Analytics?

The main focus of BI is the analysis of historical and current data. BI provides a process for analyzing / comparing past events, the current situation and business development over a certain period. Using this tool it is possible to find the cause of the problem and determine the necessary actions to fix it. The main focus of BA is predicting future results. Using this tool it is possible to determine the consequences of an unresolved problem in the future, possible strategies and predict the results.
BI works with past and current data to determine business needs. BA works only with past data to predict business needs, customer needs and drive productivity.
The task of BI is to provide a vision of current business operations, information on current trends and customer interests. The task of BA is to provide information on changes in business operations, increased productivity, future customer needs.
Basic BI tools: Qlik Sense, SAP Business Objects, TIBCO, PowerBI, etc. BA tools: Word processing, Google documents, MS Office tools, MS Visio, spreadsheets, etc.
BI is a tool mainly for large companies whose goal is to ensure smooth running of business. BA is a tool for small companies to develop business, improve efficiency and achieve goals.
BI uses more User Interface Dashboards for analysis and carrying out operations, BA uses more tools and software applications.
BI methods and technologies for data analysis: reporting, real-time analytics, dashboards, online analytical processing, etc. BA methods: predictive modeling, data modeling, requirements modeling, SWOT analysis, etc.

BI and BA perform different tasks and roles in the business optimization process. However, both tools are an important and integral part of operational activities for better understanding the business, ensuring an efficient decision-making process, optimizing business processes, predicting trends and gaining a competitive advantage.

10 up to date BI terms

Man of today is an active user of advanced technologies. Every day each of us deals with different technologies that lighten and improve the life: internet, smartphones, messengers, cloud data warehouse, online banking etc. Business also doesn’t stand apart and implements different technologies to optimize its operative activity. It’s difficult to imagine that all these technologies can get along without analytics. In spite of its not always proper and effective usage, the practice necessity is out of questions and doubts. Data is a valuable company resource that deserves attention and analytics the relevance of which continues to grow. Let’s consider 10 key words of business intelligence 2021.

Embedded Analytics – analytical content integration into business process applications. With the help of this decision there appears possibility to get all necessary information. In addition, users work efficiency and ability to solve complex business tasks improves.
Cognitive computing – this is the trend of the last few years, without doubt. Every day «tons» of data are generated, and work with it is overwhelming for human brains. The main ability of this technology is to process a huge amount of structured and unstructured data and transform it into valuable information.
Data accuracy – effective business decisions can be made using accurate data. This is the first and the main rule of the valid business analytics. Understanding it company management begins to focus more on an organization of the effective data management process. For example, COVID-19 situation has shocked the whole world and affirmed the importance of accurate data ownership. All people wanted to know the truth concerning number of infected, vaccinated, mortality etc.
Decision intelligence – new and important discipline of artificial intelligence age that is oriented to transform information into the best practical solution. This discipline is a complex of data science, social sciences, decision science and management science. Using decision intelligence organizations can improve their operative activity, literacy, project management effectiveness, strategy management and decision-making procedure.
Predictive analytics – is indispensable business tool. Advanced analytics technologies give an ability to forecast event and decisions results that help to prevent risks or mitigate effects. This tool promote business to reach new level.
X-Analytics – not always data analytics relates to numbers. Analyzed business data can be in different types: text, video, audio etc. To gain competitive advantage it is necessary to analyze such data too. X-Analytics sense is to make different type analytics of structured and unstructured data (video analytics, text analytics, audio analytics etc.).
Mobile Analytics – the growth of mobiles usage has instigated appearing of mobile analytics necessity. Mobile decisions implementation and usage in business give many benefits specifically data access in any time and in any location. Also, mobile analytics allows companies to get access to data in real-time mood in any place.
Augmented data management – the task №1 is to provide data security and quality for real-time analytics. But it is quite difficult to make. In this case augmented data management helps to realize it. Gartner’s and Deloitte’s reports affirm this trend and accent benefits that could be achieved by artificial intelligence and data management combining.
Prescriptive analytics – users’ prescribing all possible actions that they have to perform. The main task is to help specialists to choose the correct predictive model for the best decision achievement.
Collaborative BI – is the combination of social media and other advanced technologies with online business analytics. It serves as a powerful tool for business to make rapid and effective decisions.