As the fields of business intelligence and business analytics continue to develop and grow, organizations must be aware of the distinctions between the terms and understand their value. Adoption and usage of business intelligence and analytics tools show no sign of slowing. Understanding these concepts is vital to making the best business decisions, to maintaining a competitive edge across all industries, and to enabling companies to capture operational and strategic value.
To learn more, see the infographic below created by Pepperdine University’s Online MBA program.
Distinguishing Business Analytics and Business Intelligence – Resource from Pepperdine University
Differences Between Business Analytics and Business Intelligence
The goal of business analytics is to develop successful analysis models. It simulates scenarios to predict future conditions. It is a very technical approach to predict upcoming trends. This process helps find patterns after analyzing previous and current data. The analysis is used to devise future courses of action. Professionals working in this field use data mining, descriptive modeling, and simulations.
Business intelligence uses different types of software applications to analyze raw data. Professionals working in this field study business information. They closely consult with decision-making managers. They identify existing business problems and analyze past and present data to determine performance quality. They use KPIs and other metrics, and prepare easy-to-read reports. The reports give unique insights into the workings of the business and empower organizations to make optimum business decisions.
Business analytics experts help predict what is going to happen in the future. They use data to analyze what will happen under certain specific conditions. They can predict the next trends and outcomes.
Business intelligence experts, on the other hand, help track and monitor data and business metrics. They can correctly identify what happened and what is happening now. They can discover why something happened, how many times something happened, and when all such events took place.
Data-Focused Talent Shortage
Very few managers have high expertise in data fields because the use and analysis of big data has emerged only in the last few years. Even new managers and leaders do not have requisite skills to devise data-driven digital strategies. Most organizations need a new kind of talent base that is well versed in the data-driven business landscape. One McKinsey report estimates that by 2018, the US will face a shortage of 140,000–190,000 data science professionals. Even now, companies must pay very high salaries to employ data analysts. Only large companies can afford such professionals.
The Future of Big Data Analytics
While 78% companies agree that big data will impact their business, only 58% think their company is ready to take advantage of all the potential that big data offers. The reason for this is not difficult to ascertain. Companies must use various techniques to capture data, and the data collected must be realized in a specific format. Data analysts must use exacting methods and processes to analyze this data. Capturing and analyzing big data is a complex process and can be handled only by trained data analysts.
Benefits of Business Analytics
Engaging effective business analytics is necessary to make the right business decisions. Managers with proven analytics skills are better able to plan for future projects. The biggest advantage involves forecasting. Analysis of previous and current data helps predict future trends. This information is crucial to the success of a business. A company may have different types of products. It may keep promoting the fast-selling product while another product that is quickly gaining traction may remain under the radar. Only big data analysis can reveal the importance of the latter product. Business analytics is a forward-thinking way to improve operational efficiency. Decisions can be made faster, and it becomes easier to make sense of large volumes of data.
Benefits of Business Intelligence
Business intelligence proves useful in identifying new opportunities. A company can identify a new market that holds important business opportunities. Product pricing can be tweaked to market demands. Business productivity can be improved. Sales and marketing expenses can be optimized. Business intelligence helps predict customer behavior, which proves useful in improving customer service.
Usage and Adoption of Big Data
Even when the benefits are well known, very few companies are able to use big data analysis in a significant way. Almost 50% of businesses face difficulty in the field of business analytics. They are unable to ensure the quality of data. Without the right talent to manage and analyze data, they are at a disadvantage in the market. Many businesses rely on simple applications to analyze data. These tools are not very effective in analyzing big data. This type of data must be analyzed scientifically. It is a complex job that can be handled only by professionals who possess training and skills in data analytics.
Developing a Big Data Analytics Culture
All types of businesses are working continuously to take advantage of big data. They are using simple as well as complex solutions to work with such data. There is a consensus realization that a high level of data analytics is necessary to ensure business success in today’s market. Now, companies are incorporating data analytics into all their departments. They are using sophisticated tools and solutions to predict future trends. Almost 82% of business executives now take advantage of data-driven reports and dashboards.
Sources of Big Data
Big data is obtained from a wide range of sources. Sales records and financial transactions generate a great volume of useful data. They help devise pricing models for different types of products. The customer database is a key source of data. Large amounts of contact details and other data can be mined from emails, productivity and communication applications. In fact, every business process generates data. All such data must be collected and stored properly.
Businesses need the services of both business analytics and business intelligence experts. There are differences in their positions, but both groups play important roles in the success of a business. As more and more businesses rely on digital strategies, they have to analyze their big data properly and effectively. They need the support of trained and skilled data analysts to help achieve the best business success possible.
To identify threats and opportunities, analysts may look through thousands of data records manually, or define KPIs and make a discovery literally in a few clicks. Which approach will your business choose?
According to Richard Branson, a business magnate and investor, “business opportunities are like buses, there is always another one coming.” The idea seems convincing: however, there is hardly a person who did not feel disappointed when they missed their bus. Likewise, companies prefer not to miss their opportunities. But how to recognize them well in advance?
In fact, a company can identify threats and opportunities with the help of business intelligence. Here, we will not focus on the simplest, but highly inefficient approach of scrolling through thousands of data records. Instead, we will dwell on the approach of defining relevant KPIs, which BI consulting practitioners advise.
As the challenge described is not industry-specific, let’s consider a large product portfolio (100+) – an example relevant to several industries (for example, retail and manufacturing). Now, let’s take a closer look at how business intelligence and data analysis can help in defining KPI metrics and in finding opportunities and threats related to a particular product.
Prepare BI infrastructure
As a business has to deal with a big volume of data, usually taken from numerous sources, in order to reach the data, a company needs to implement BI infrastructure. This requires using a tool that is capable of connecting to multiple data sources from which data is combined to create OLAP data models for slicing and dicing. At this stage, to build a required BI infrastructure and ensure data quality, companies may reach out to business intelligence consulting experts.
Start with the right approach to developing KPIs
The next step is to define KPIs. At this stage, it’s crucial to have a clearly defined strategy and know how to translate it into right KPIs to create a hierarchy where lower levels support higher ones. Thanks to historical data analysis and forecasting, business intelligence allows companies to define metrics and set KPI targets, both long-term and short-term.
Track the dynamics
In a constantly changing environment, it is important to keep track of the dynamics. The following KPIs may be useful for this purpose.
1. Absolute figures
With absolute values, it’s possible to look quickly at best (or worst) results in a few clicks. A simple filtering will put the required information to the top. Having right dimensions and measures, a company will easily learn, for example, what product brought highest (or lowest) sales and margin.
2. Relative figures
Let’s imagine that one of the products from the portfolio shows -2% of sales. Undoubtedly, a decline in sales is not what a company is happy to see. But is this decline alarming? To understand that, you need to look at the portfolio in general:
Product 1: -2%
Product 2: -2.5%
Product 3: -3.2%
Product 4: -5.4%, etc.
When compared with others, Product 1 looks the best, while Product 4 looks problematic, as its sales decrease faster. Besides, there is an overall decline. Correspondingly, a company will focus on improving its overall performance.
3. Right time frame
Choosing the wrong period to measure performance may lead to distorted results. For instance, a company takes the period of last 2 weeks when the sales are growing. But if we look at last 10 weeks, we’ll see a decline followed by a slow recovery.
To avoid serious fluctuations that seasonality brings, it’s necessary to define a seasonal coefficient for each month (for example, Jan: 1.0, Feb: 0.98, Mar: 1.0, …, Jun: 2.5, Jul: 3.2, …) and apply it to the values (for instance, sales). This simple measure will help to get season-neutral values.
Compare Target vs. Fact
How can a company know if a 5-percent growth is enough? It depends on what they defined as good. For that, a company should set a target for each product, as some products cannot (or should not) grow while others are expected to do it. A larger company may need to set more sophisticated targets for every product and region combination. For example, Product 1 should grow fast in TX and CA, while product 34 in NY and PA.
To sum it up
To cope with the challenge of identifying threats and opportunities, a company needs KPIs oriented towards finding these valuable insights. Business intelligence can be a helpful tool for defining these KPIs, and an implemented BI solution will allow filtering, grouping or sorting in a few clicks, instead of scrolling through thousands of lines.
Business Analytics vs. Data Science
“Business analytics” and “data science” — are they basically interchangeable terms, or entirely separate professional pursuits? There’s certainly overlap on the topic of Big Data and using data to inform decisions. There is no dispute over the fact that both business analysts and data scientists use exponentially growing sources of data to do their work. [Check out PARIS Tech’s recent post on Big Data]
An article and featured infographic by Angela Guess for Dataversity.net argues that the terms business intelligence and data scientist are distinct, and not just because one pursuit applies to business, and the other to scientific results.
Click below to read the original article which accompanies the business intelligence vs. data scientist infographic.
Infographic: Business Analytics v. Data Science
What is Business Analytics?
Science and business continue to intersect, most recently on the topic of data analytics. Generally speaking, “data analytics” is the process of organizing and interpreting data to uncover valuable information. “Business [data] analytics” is the more specific application of data analytics to business purposes.
Some examples of data analytics might be: What segment of customers use desktop v. mobile? Or, which target audience found value in the most recent advertising campaign? Companies ranging from Target to Google find results from these kinds of questions so valuable that they pay data analysts over $100,000 per year. To learn more about the burgeoning data analytics industry, check out this educational resource, created by Villanova University’s Master of Science in Analytics program.
OLAP and Hadoop: A Great Pairing
OLAP continues to be a relevant and exciting technology, most recently in pairing OLAP and Hadoop. As we are OLAP.com, we have ALWAYS seen the value of OLAP technology. We admit OLAP has been a bit out of style the last few years. Some companies even run Google ads about how “OLAP is obsolete,” but nothing could be further from the truth. (Check out our blog on that one.)
We see this in the fashion industry all the time: what is old is new again! This is rare in the technology realm, but it seems to be the case with OLAP. As developers struggle to get value out of Hadoop data, they discovered they needed the speed and flexibility of OLAP. OLAP and Hadoop is a powerful combination for getting to the ultimate goal of extracting value from Big Data.
Bringing OLAP to scale for Big Data
In an article from ZDNet, Is this the age of Big OLAP? Andrew Brust writes about the new relationship between OLAP and Hadoop. He highlights that OLAP technology can be particularly beneficial when working with extremely large Big Data sets. Typically, OLAP has not been scalable enough for Big Data solutions. But OLAP technology continues to progress, we find this new application of OLAP exciting. Brust discusses a few strategies for bringing the two technologies together. He mentions a few OLAP vendors in detail and how they manage the issue of scalability for OLAP software.
If you want to try using OLAP with Hadoop, perhaps you want to give PowerOLAP, the mature OLAP product of OLAP.com, a try? There is a free version of PowerOLAP available. If you plan to test PowerOLAP with your Hadoop, contact PARIS Tech, and they will lift the member limit for you in the free version, as you will need to go beyond the member limit that ships with the free version.
In sum, OLAP.com is pleased to see OLAP rising in relevance once again and getting some of the recognition we felt it deserved all along. It is a testament to the power and value OLAP has as a technology.
The Power of OLAP and Excel
Should Excel be a key component of your company’s Business Performance Management (BPM) system? There’s no doubt how most IT managers would answer this question. Name IT’s top ten requirements for a successful BPM system, and they’ll quickly explain how Excel violates dozens of them. Even the user community is concerned. Companies are larger and more complex now than in the past; they are too complex for Excel. Managers need information more quickly now; they can’t wait for another Excel report. Excel spreadsheets don’t scale well. They can’t be used by many different users. Excel reports have many errors. Excel security is a joke. Excel output is ugly. Excel consolidation occupies a large corner of Spreadsheet Hell. For these reasons, and many more, a growing number of companies of all sizes have concluded that it’s time to replace Excel. But before your company takes that leap of faith, perhaps you should take another look at Excel. Particularly when Excel can be enhanced by an Excel-friendly OLAP database.That technology eliminates the classic objections to using Excel for business performance management.
Excel-friendly OLAP products cure many of the problems that both users and IT managers have with Excel. But before I explain why this is so, I should explain what OLAP is, and how it can be Excel-friendly. Although OLAP technology has been available for years, it’s still quite obscure. One reason is that “OLAP” is an acronym for four words that are remarkably devoid of meaning: On-Line Analytical Processing. OLAP databases are more easily understood when they’re compared with relational databases. Both “OLAP” and “relational” are names for a type of database technology. Oversimplified, relational databases contain lists of stuff; OLAP databases contain cubes of stuff.
For example, you could keep your accounting general ledger data in a simple cube with three dimensions: Account, Division, and Month. At the intersection of any particular account, division, and month you would find one number. By convention, a positive number would be a debit and a negative number would be a credit. Most cubes have more than three dimensions. And they typically contain a wide variety of business data, not merely General Ledger data. OLAP cubes also could contain monthly headcounts, currency exchange rates, daily sales detail, budgets, forecasts, hourly production data, the quarterly financials of your publicly traded competitors, and so on.
You probably could find at least 50 OLAP products on the market. But most of them lack a key characteristic: spreadsheet functions.
Excel-friendly OLAP products offer a wide variety of spreadsheet functions that read data from cubes into Excel. Most such products also offer spreadsheet functions that can write to the OLAP database from Excel…with full security, of course.
Read-write security typically can be defined down to the cell level by user. Therefore, only certain analysts can write to a forecast cube. A department manager can read only the salaries of people who report to him. And the OLAP administrator must use a special password to update the General Ledger cube.
Other OLAP products push data into Excel; Excel-friendly OLAP pulls data into Excel. To an Excel user, the difference between push and pull is significant.
Using the push technology, users typically must interact with their OLAP product’s user interface to choose data and then write it as a block of numbers to Excel. If a report relies on five different views of data, users must do this five times. Worse, the data typically isn’t written where it’s needed within the body of the report. Instead, the data merely is parked in the spreadsheet for use somewhere else.
Using the pull technology, spreadsheet users can write formulas that pull the data from any number of cells in any number of cubes in the database. Even a single spreadsheet cell can contain a formula that pulls data from several cubes.
At first reading, it’s easy to overlook the significant difference between this method of serving data to Excel and most others. Spreadsheets linked to Excel-friendly OLAP databases don’t contain data; they contain only formulas linked to data on the server. In contrast, most other technologies write blocks of data to Excel. It really doesn’t matter whether the data is imported as a text file, copied and pasted, generated by a PivotTable, or pushed to a spreadsheet by some other OLAP. The other technologies turn Excel into a data store. But Excel-friendly OLAP eliminates that problem, by giving you real-time data for a successful BPM system.
To learn more about OLAP, click here.