Infographic: Business Analyst vs. Data Scientist

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.

infographic-business-analyst-vs-data-science

Click below to read the original article which accompanies the business intelligence vs. data scientist infographic.

Infographic: Business Analytics v. Data Science

 

Seeing Value in OLAP Anew: OLAP and Hadoop

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 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 Some companies even run Google ads about how “OLAP is obsolete,” but nothing could be further from the truth.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.

OLAP and Excel

OLAP and Excel

 

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.

Introducing OLAP
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.

28% of a Data Analyst’s Time is Spent on Data Prep

Data preparation

 

James Haight of Blue Hill Research recently wrote a blog post that breaks down the costs and numbers of Data Preperation. Typical reports focus on hours and efficiency. As stated by James Haight, “At Blue Hill, we recently published a benchmark report in which we made the case that dedicated data preparation solutions can produce significant efficiency gains over traditional methods such as custom scripts or Microsoft Excel.”

According to their study, data analysts spend on average about 2 hours per/day just on data preparation alone. When Blue Hill Research factored in the average salary that a data analyst makes in the U.S., it comes out to be around $62,000 per year. After doing the rough calculations, they figured out that 28% of a data analyst’s time is spent preparing data, which equals about $22,000 worth of their yearly salary. While that number seems high just considering one analyst, you can imagine how drastic that number looks when you figure in how many data analysts there can be at larger corporations. In this post, they breakdown the numbers even more. For example, say a company has 50 data analysts which is estimated that $1,100,000 is spent annually just on preparing data.

In order for data analysts to shift their full attention to where it should be, “analyzing the actual data”, there needs to be a solution implemented. PARIS Technologies has the solution. PowerOLAP is a software that was designed to take the stress and time out of preparing and comparing data. It has the capability to aggregate information from any source into a multidimensional PowerOLAP database. It empowers users the flexibility to slice and dice with ease. Learn more about how PowerOLAP could be the solution for your company facing this problem.

 

To read the Blue Hill Research post, click here.

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.

The Problem and Solution in the BI Market

A recent post on the PARIS Tech Blog, The Problem in the BI Market, highlights a few key facts/issues in the BI market.  Some of these include disconnected applications, disconnected IT and front-end users, and slow processing times.

Most interesting is the emphasis on connectivity between applications becoming important in the BI market.  Presently, most firms struggle to move data collected in one system into another for reporting or delivering to upper management. PARIS suggests that software products will begin to prioritize more and more the ability to collaborate across platforms—sharing data smoothly from one place to another.

The PARIS Tech post discusses IT and end-users being able to use their preferred applications and a shared data set, which leads to the next interesting bit, their emphasis on collaboration and productivity.  The software products that will succeed in the future BI market will be those that allow people to easily collaborate because their applications will be connected.  They will also automate manual processes and calculations that are performed in Excel today.

Improved collaboration and productivity influence peoples’ lives for the better and makes them enjoy their work more, while they also drive the business towards profitability.

Read the full post on the PARIS Tech blog

Read about using your preferred application and a shared data set with Olation.

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