Here’s a five-step process you can use to get started with data-driven decisions.

1. Look at your objectives and prioritize. Any decision you make needs to start with your business’ goals at the core.
2. Find and present relevant data.
3. Draw conclusions from that data.
4. Plan your strategy.
5. Measure success and repeat.

Statistics (or statistical analysis) is the process of collecting and analyzing data to identify patterns and trends. The online technology firm TechTarget.com describes statistical analysis as an aspect of business intelligence that involves the collection and scrutiny of business data and the reporting of trends.

Statistical research in business enables managers to analyze past performance, predict future business practices and lead organizations effectively. Statistics can describe markets, inform advertising, set prices and respond to changes in consumer demand.

Businesses can use data analytics to improve management in many ways. Businesses can analyze their competitors in real time, so they can adjust prices, make offers that are better than their competitors’ sales, and even analyze a competitor’s negative reviews to determine how they can out-perform that competitor.

Data analysis is important in business to understand problems facing an organisation, and to explore data in meaningful ways. Data in itself is merely facts and figures. Data analysis organises, interprets, structures and presents the data into useful information that provides context for the data.

Data analytic techniques enable you to take raw data and uncover patterns to extract valuable insights from it. Today, many data analytics techniques use specialized systems and software that integrate machine learning algorithms, automation and other capabilities.

Depending on the stage of the workflow and the requirement of data analysis, there are four main kinds of analytics – descriptive, diagnostic, predictive and prescriptive.

Here are 5 types of big data analytics:

• Prescriptive Analytics. The most valuable and most underused big data analytics technique, prescriptive analytics gives you a laser-like focus to answer a specific question.
• Diagnostic Analytics.
• Descriptive Analytics.
• Predictive Analytics.
• Outcome Analytics.
• The Implication.

To improve your data analysis skills and simplify your decisions, execute these five steps in your data analysis process:

• Step 1: Define Your Questions.
• Step 2: Set Clear Measurement Priorities.
• Step 3: Collect Data.
• Step 4: Analyze Data.
• Step 5: Interpret Results.

90% of organizations today use descriptive analytics which is the most basic form of analytics.

Three key types of analytics businesses use are descriptive analytics, what has happened in a business; predictive analytics, what could happen; and prescriptive analytics, what should happen.

These solutions can be categorized into three main types – Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics.

THE BENEFITS OF BUSINESS ANALYTICS

• More Informed Decision-Making. Business analytics can be a valuable resource when approaching an important strategic decision.
• Greater Revenue. Companies that embrace data and analytics initiatives can experience significant financial returns.
• Improved Operational Efficiency.

If we refer to various industry studies, Business Analytics has emerged as one of the most lucrative career options in terms of salary and growth. In fact, ‘Data Scientist’ has consistently featured in Glassdoor’s “25 Best Jobs in America” for the past couple of years.

And if you’re reading this and wondering whether a BA career is for you, be warned: business analysis is hard – really hard. But as long as you’re prepared for that, and as long as you have, or think you can learn, the above skills, then welcome aboard!

Rather, it’s the future of business analytics. Gartner predicts the deployment of AI in business analytics to be the second most important technology trend in the forthcoming year. In fact, by the end of 2020, they predict that more than 40% of data science tasks will be augmented and automated.

Quite simply, Business Analytics is inclined to on deriving an analysis of data to act accordingly and MBA focuses more on business planning, expansion and improving management.

The impossible will become possible, and this may well lead to an autonomous decision-making process. Data analytics is expected to radically change the way we live and do business in the future. Already today we use the analytics in our technology devices, for many decisions in our lives.

The rise of big data has led to a boom in business analytics careers and, along with that, a growing number of people seeking an MS in Business Analytics. In fact, Glassdoor identified data scientist as one of the “25 Best Jobs in America for 2016” with an above-average median salary of $116,840.

Here are some of the top skills for data analysts to acquire for success in their field:

• SQL.
• Spreadsheets.
• Critical Thinking.
• Statistical programming languages.
• Data visualization.
• Public speaking.
• Machine learning.
• Data warehousing.

Business analysts earn a slightly higher average annual salary of $75,575. Business analysts tend to make more, but professionals in both positions are poised to transition to the role of “data scientist” and earn a data science salary—$113,436 on average. Skillsets.

What is business analytics? Business analytics takes a data-driven approach to the world of business, using statistics and data modeling to develop new business insights. This blend of technology and business makes it an ideal study option for anyone with an interest in programming or working with big data.

Business analysts do deal with IT professionals and IT processes. But they are not involved in coding/programming, that’s the job of programmers. Business Analysts deal with requirements understanding, development and management and functional testing. These responsibilities don’t involve any programming skills.

While the ability to program is helpful for a career in analytics, being able to write code isn’t necessarily required to work as an analytics professional. Apart from the above languages, statistical software such as SPSS, SAS, Sage, Mathematica, and even Excel can be used when managing and analyzing data.

Data Science involves a lot of coding skills whereas Business Analytics does not involve much coding. Data Science is a superset of Business Analytics. Data Science analysis results cannot be used in day to day decision making of the company whereas Business Analytics is vital in management taking key decisions.

Overall, a data analyst and a business analyst have many parallels and require an analytical mind, proficiency in Excel, and strong communication skills. They differ in that a data analyst typically has a more mathematical or statistical mindset, while a business analyst has more of a business mindset.

Business analysts role, therefore, might require Python skills on most times, while not requiring it at all at other instances. But they are all analysts not necessarily dealing with quantitative data.

Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources.