Boost Your Business Intelligence Career With Data Science

Boost Your Business Intelligence Career With Data Science

By Jennifer Marley

Every day the world generates more than 2.5 quintillion bytes of data. Many companies do not know what to do with this influx of data, and therefore hire people with specialized skills to collect, store, process, analyze, and develop business intelligence and actionable solutions based on this information. These people are known as data scientists, and the demand for the job title is extremely high. According to research by LinkedIn, data science and machine learning jobs are the hottest and most in-demand career paths in the world. Six out of the top 15 emerging jobs are related to data science skills and an estimated 2.7 million job postings for data analytics and data science are projected by 2020. The world is shifting, and businesses are no longer looking simply for business intelligence analysts, but rather modelers, mathematicians, engineers and scientists who can turn data into actionable information. Data scientists hold some of the most sought-after positions in the world, and if you want to boost your business intelligence or graduate career, it’s important that you boost your resume with big data.

Benefits Of A Career In Big Data

Big data has been a hot buzz word in business for the past couple of years. Big data jobs are very lucrative and pay more than a typical business intelligence job. With a career in data science, a new graduate can expect to make about $20,000 more than a typical business intelligence analyst and generally earn about $95,000 to $165,000 a year. The starting salary is extremely high for graduates and can definitely help you deal with past financial commitments, start a new life, and even start a family. These jobs tend to be at the top tier of business, making it easier to be promoted to the highest levels of your organization. Not only will you make more money and move faster up the ladder, but you’ll also be at the forefront of cutting-edge technology. A career in data science exposes you to the latest in technology and allows you to constantly boost your resume. Furthermore, data science expands past the realm of business. If you leave the world of business, your skills will transfer to a great many other jobs: from law enforcement to the medical field, every career uses data and needs a way to understand and analyze it.

New Developments In Data Science That Will Boost Your Resume

Since data science is still a fairly new field, the skills and needs associated with the role are ever-changing. The data evolution is rapidly evolving, and you need to ensure you’re up-to-date with the most in-demand skills. In 2019, the focus of data science is expected to home in on bringing the digital world into the physical world. Self-service business intelligence dashboards are all the rage, and therefore you’ll need to learn tools such as Tableau, Qlik Sense, Power BI, and Domo. Companies simply want to cut out the business analyst and have data scientists provide them with a real-time dashboard that can deliver information about sales and tell a story about the company’s progress instantaneously. Furthermore, mobile dashboards are expected to be in greater demand over the next year, which will require data scientists to focus on mobile app and development experience. Lastly, the R programming language will also make a huge impact in 2019. R scripts can be audited, and are easily rerun by business managers, creating replicable and valuable analysis. The R language is also strong in supporting machine learning, which is expected to reign supreme in the world of data science and business analytics in the coming year and beyond.

Skills That You No Longer Need To Focus On

Once upon a time, Hadoop was the hottest tool for a data scientist. It was the ultimate answer to storing and processing big data. However, based on cost and complexity, a lot of businesses are shying away from the once popular tool. Now, Hadoop is serving as a storage repository for processing ETL batch workloads rather than for interactive, user-facing applications. Instead, data scientists are focusing on Hadoop alternatives such as Apache Spark, Apache Storm, Google BigQuery, Hydra, Ceph, and DataTorrent RTS. Although Hadoop isn’t expected to completely disappear, you should focus your time and energy on learning these new alternatives as Hadoop will likely become consigned to a niche and legacy system in the future.

A career in data science will set you apart from the rest in the field of business intelligence . With so many new graduates attempting to enter the business world, you will need a leg up, and big data is the way to go. As companies get further into the digital world and harness more and more data, you’ll become an essential member of any team and a highly sought-out individual. In general, a career in data science is the number one ingredient in a recipe of success in business intelligence.

[Infographic] Business Analytics : The Intersection of Science and Business

[Infographic] Business Analytics : The Intersection of Science and Business

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.


Seeing Value in OLAP Anew: OLAP and Hadoop

Seeing Value in OLAP Anew: OLAP and Hadoop


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, 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, 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, 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.

Infographic: 6 Business Intelligence Best Practices

Infographic: 6 Business Intelligence Best Practices

Be Mindful to Keep these Practices throughout your Business Intelligence Project

Getting Business Intelligence Best Practices to work well is challenging.  Check out the 6 best practices outlined in this Infographic. Check it out and see how your organization stacks up. It’s easy to think that bigger is better, or assume you need to start from scratch. Actually its about having BI policies in place, keeping everyone on the team involved, having a clear scope, and making sure everyone is trained up well.

It is also important for a Business Intelligence project to have an analytics model that reflects the organization.

6 Things to Help you Tackle IoT and Big Data

6 Things to Help you Tackle IoT and Big Data

Rear view of business person in ready position on start line to compete

So, you have made the decision to dive into the world of IoT and Big Data? Where to start is the major question and can seem overwhelming. Preston Gralla has come up with some key steps in making the decision or updating your current solution program in his article, 6 Tips for Working with IoT and Big Data.

  1. The first clear way to dive in is to know the problem you are facing and what the end result looks like to you. Without a crystal clear picture of what you have to solve, a project can easily head in a different direction or take longer than expected to go into implementation.
  2. Then, you must deploy the “right people” on the project. Gralla states that Data scientists can be expensive to employ and hard to come by, because, today, they are so much in demand. Instead he suggests that you seek the resources within your company. Employees with the Big Data and IT experience may have the drive and motivation to learn new techniques in order to take on new projects.
  3. Next, Gralla talks about how important it is to know exactly which data you will collect and also how it will be stored. In order to get the most from your analytics, it is important to be working with precise data that will give you the most accurate results and ROI.
  4. Data can be made up of complex layers and other times, it can be a simple layer of information. To ensure that it will work compatibly with each other, Gralla suggests that businesses build an extra abstract layer to allow room for extra layers of data that you may encounter along the way.
  5. The fifth tip that Gralla suggests, has to do with the platform. A large data analytic platforms can be very expensive and take extra time to develop. It may be the most efficient to invest in an outside platform for analytics and even maybe a cloud-based system.
  6. And finally, the last bit of advice when tackling Iot and Big Data is to start with a manageable size and continue to grow from there. Many businesses will take on too much at one time and struggle to succeed at them. Instead, start small so that you can manage to smooth out any errors along the way before taking on more.

To read the whole article, click here.

BIG Data Road Blocks to Tackle

BIG Data Road Blocks to Tackle

Businessman jumping over hurdle

As I think most of us would agree, Big Data has made big leaps in providing the business world with a large advantage. Luc Burgelman does a great job of identifying the three hurdles that he believes are holding businesses back from reaping the most benefit from their Big Data, in his article.

The first hurdle Burgelman refers to is the ability to evolve. He brings up a great point, companies are looking for different things than they were just two years ago. They need to take technology further than before to accomplish what they need as a final result. Also, companies need to be able to engage different/more departments in the analytical process. It is no longer just about the IT team. Other departments have valuable assets to add to the equation of data anaylsis, and we have to be open to sharing the data across the departments and company, taking a more well-rounded approach to tackling large analytical processes. Which leads smoothly into Burgelman’s second point…

Not only is it important to be involving more of the company’s departments, but we need to make sure that the C-level Executives are equally “on-board.” Let’s face it, without their their final “blessing,” no data technology plan will hit the ground running and be successful. Executives need to be equally passionate about the technology and understand the great benefit and ROI of the analytics behind the data.

The third hurdle that Burgelman talks about is changing the mindset of not only the C-level executives but of all who work directly with the data such as the data users and data scientists. Big Data and the technology behind it is a game changer and offers greater benefits to customers, which returns in greater customer loyalty and greater sale margins. Companies need to be able to change and progress with the latest technologies and analysis software to be able to change the way people and businesses make their decisions and interact with their data. So what do you think, are these hurdles something that we can get over and allow businesses to run faster.

Want to read the full article? Click Here.