Distinguishing Business Analytics and Business Intelligence

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

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.

 

11 of the Best Practices for Business Intelligence

Business Team Brainstorming Data Target Financial Concept

Dennis McCafferty of CIO Insight recently wrote an article that addresses 11 of the top practices of Business Intelligence. With Business Intelligence controlling such key factors in today’s companies such as, analytics, business performance management, text mining and predictive analytics, it is crucially important to understand it. Let’s take a look into CIO Insight’s 11 best practices and see if you are already taking advantage of these.

  1.  Bigger Isn’t Always Better: Just because a solution can gather a large amount of data doesn’t mean that they are helping you get the most out of the data. McCafferty thinks that trustworthiness and immediacy are the key elements.
  2. Deliverable Value Over TCO: When your BI solution can deliver specific ROI, you will gain higher buy-in no matter the initial total cost of ownership.
  3. Take Stock of Current Resources: Taking advantage and leveraging the IT that your company already owns to support your BI solution is a top practice. You can then utilize that spending on something else that will make a larger impact.
  4. File-Formatting Resources: Since Business Intelligence uses more than 300 file formats, it is important that you are prepared and ready to use any one of them.
  5. Create BI Policies for Deployment: It is important to have BI policies in place such as how the data is collected, processed and stored. This will ensure higher level of relevance and accessibility.
  6. Go Team, Involve Business Leaders From the Outset: You need to remain on the same page as all of the different leaders and work as a big team to keep IT on the right path.
  7. The Only Constant? Change: Every thing is constantly changing and evolving so this will continue to test your BI deployment at all times.
  8. Limit Initial User Participation: It is better to start out slow and steady when introducing initial users. If not, it can lead to confusion, errors and confusion which will impact BI’s final impact.
  9. Define the Project’s Scope: A BI implementation should be taken in stages and a company must know how many users and functions will be needed over time.
  10. Training Day: In order for your BI project to be a success, you must take the right approach to training employees and make sure that they are properly educated and feel comfortable using the new solution.
  11. Support Self Service: The goal of BI is to pass along the project to the appropriate department. In order to do this you must support the training plans and keep this practice as a priority at all times.

 

Click here to read the original article.

Healthcare and Big Data are Not Slowing Down

Doctor sitting at office desk and working on his laptop with medical equipment all around top view

The days of paper charting medical records are long gone. Michael Morrison gives a good look into why Big Data is vital to the Healthcare industry in his post, “Big Data Remains Hot in Health Care.”

Medical firms did not have much choice in adapting to these changes after the Health Information Technology for Economic and Clinical Health Act in the US in 2009 took effect, this was one of the largest efforts ever in improving patient care, ensuring proficient operations, and closing the gap of medical error. The result has been phenomenal and continues to progress.

With the ability of the medical industry to continuously run analytics, they gain critical information from high volumes of medical records and data. Morrison mentions that with advancements in patient data analytics, it is becoming the most important aspect of implementing medical analysis, which allows for patient care to be personalized. Many common road blocks that are normal in the course of  patient care and health management can be corrected with the use of Big Data tools.

The National Cancer Institute has created a Big Data project that has, through Big Data research, improved cancer treatments. They are now able to learn a lot about the patient’s responses to different medications and choose the best treatment course for a specific patient. The implementation of data solutions across the entire medical industry benefits everyone involved from the provider to the patient. The next time you make a visit to your local doctor’s office or hospital, take a look around and notice how much we rely on the ongoing advances of technology. Where would be now without all of it; how much safer has it made our treatment as well as our loved ones?

Click here to read entire article.

Advice for CFOs: Invest in New Technology

Top Technology Trends for Today’s CFO’s” is another insightful post from a blogger we frequently feature, Timo Elliott. In it he admits that the CFO relationship with the CEO and other business executives leaves something to be desired.  He recommends that CFOs invest in the latest technology, which will increase productivity with real-time updates and continuous forecasting.

cfos-and-speed-608x456

{Image from Timo’s post, link to http://timoelliott.com/blog/2015/07/top-technology-trends-for-todays-cfos.html}

Elliott mentions a combination of new technology including: in-memory computing, big data, the cloud, and mobile.

He homes in on a key point—that finance staff at large companies are extremely bogged down with just the basics of maintaining their financial reports. As Elliott puts it, “Staff have to spend too much time on basic duties and have no time to improve their understanding of the operational measures that drive and impact financial measures.” This lack of insight or understanding of how the operational measures drive and impact financial measures is the root of the relationship problem between CFOs and other business executives.

Elliott suggests new in-memory computing technology because, “they reduce complexity by combining real-time actuals with budgeting and analysis in a single, integrated system. Financial data is stored just once, making almost every aspect of financial operations faster, simpler, cheaper, and more effective.” We couldn’t agree more, as developers of a new in-memory technology ourselves.

The result of improved systems, improved speed, and better data is ultimately a better working relationship between business executives, and a more productive, effective workplace.

Read Timo Elliott’s post here

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If You Think OLAP is Obsolete… Think Again!

If you think OLAP is Obsolete,

A couple things could be possible:

You don’t know what OLAP is.

You haven’t seen a recent OLAP application.

You never tried to do the knitty-gritty of business planning.

You’ve resigned yourself to doing manual labor in Excel spreadsheets and pushing to a dashboard.

OK, so I’ll give nay-sayers of OLAP a little bit of credit, OLAP (historically) can be frustrating.  Especially when some implementations are hardly what we consider to be “online”.  Most of what people are calling OLAP technology is not really connected in a live way to the data source.  There is a batch process to update the data from relational source to a Proprietary OLAP cube.

Now that is where you lose most of the IT people.  Ugh OLAP, really? IT Teams need a proprietary system to maintain like a hole in the head, because they are already underwater just trying to make sure all the delicate connections between systems are running. They just want to know that processes are able to finish running, and that their end-users are eventually served with the data they need.

The new batch of more evolved OLAP systems will address all those painful processes and more.  And how dare anyone call that obsolete?! That is the opposite of obsolete—it is what you need—a platform which allows you to:

1.) Connect the relational source to the “cube” or multidimensional modeling space

2.) Consolidate several data sources

3.) Easy access to consolidated source data in ANY front-end, and

4.) Live, real-time updates in a collaborative environment.

Never heard of an OLAP product that can do all that? Check out the Olation eBook for a quick intro. The most exciting things in OLAP have yet to come!