Tag: analytics ecosystem

Why Are BI Editors Necessary?

When you were a kid preparing for important standardized writing tests or maybe your SATs in high school, teachers often tried to drill certain strategies in your head to help boost your performance: read over the question twice, check your work, show proof, etc.

While the platform for which these techniques are implemented has ultimately shifted as you’ve transitioned into an adult, the usefulness of these tools remain the same. You still check twice or maybe even three or four times before you press SEND on your emails, don’t you?

In the business of big data, the importance of editing cannot be underestimated. Giving your big data science team a helpful hand in making sense of all your complicated, quantitative data can be a huge benefit to your company’s leadership and throughout your entire organization.

In this post, we’ll highlight the perks of having a qualified BI editor on staff.

Chief directs to his employeesTake Responsibility Away from Higher-Ups

Managers aren’t glorified crossing guards who simply point and direct traffic within your department. A major part of their responsibilities include planning for the short- and long-term while trying to optimize your current processes. And in the planning stages, editing is crucial to devise a plan that is not only sound, but also digestible. The role of a BI editor is to allow the managers who oversee data collection to continue doing their job while simplifying the message for other data scientists and managers.

Furthermore, managers sometimes take on this role by themselves and turn out to not be that effective in clarifying messages. This creates more backup and traffic within your company that takes away from everyone’s job duties.

The subject of big data analysis can be a tricky as there are several complex components that make it up. It’s likely that managers aren’t fully-equipped to discuss the minute details as they are prepared to discuss the overall strategy and business intentions.

Simplify Your Most Complex Data

BI editorProfessional and effective business intelligence editors have honed their skills in breaking down unfamiliar ideas into simpler terms. And this is a quality that’s desired in an industry full of intricate nuts and bolts.

When plans or projections have to be discussed among different departments, it’s desirable when a common language can be determined. BI editors should be able to bridge the gap between the numbers-minded data scientists and the forward-thinking, revenue-minded project planners.

Often times, higher-ups have the tendency to throw out blanket ideas without a full grasp of how to turn it into a reality. Before tasking data scientists with translation duties, a business intelligence editor can save time by turning those ideas into tangible sketches.

Smooth Out Overall Communication

Your BI editor should also be flexible among the different departments in your company. Between proposals, papers and projections, some of the best editors are able to even simplify complicated code. Programmers who are in charge of the technical background of your business intelligence solution can write code that’s more complicated than need be.

Dubbed refactoring, this tactic simplifies difficult-to-read code into a more digestible, fluid language that makes it easier for anyone who access the programming code. The BI editor’s need to comprehend analysis isn’t necessarily needed, as long as they can decipher what the programmer’s intent was and how to make the code actionable.

As many long-term marriages credit communication as the reason for their longevity, it serves a similar purpose in your business. Having a business editor to smooth out the dialogue between different departments can serve as a huge advantage by streamlining processes and developing a more unified company.


4 BI Improvements You’ve Been Waiting For

As we have successfully made it past the midpoint of 2015, the year is shaping up to offer several new enhancements in big data and analytics. Much of the improvements that come in the BI community comes in the form of either simpler, faster or more — and this improvement trickles down to businesses of all sizes across the globe.

This is why it’s exciting to see what new trends will emerge, what traditional strategies will be flipped on its head and who will be the pioneers in implementing these new improvements. Here, we’ll break down four major changes that are soon coming our way in the BI community and how to implement these strategies within your system to benefit your growing business.


Datafication is what you get when you’re able to display, track and analyze individual processes with your technology. While this isn’t necessarily revolutionary in terms of business intelligence, datafication is picking up steam with more real-time capabilities being used and valued.

DataficationA perfect example of this technology is Fitbit, the wireless, wearable activity tracker that “measures data such as the number of steps walked, quality of sleep and other personal metrics.”

It’s datafication that makes this information visible all of a sudden via a device that’s made to specifically designed to display it. This data goes a long way in tracking movements and monitoring health concerns for those trying to lose weight, stay fit or simply desire to monitor their personal health.

And this is just the base level. Datafication opens the doors for businesses to optimize tracking. The use of sensors, real-time technology and a full-fledged analytical display can serve as a tool that businesses can use to monitor their own processes with ease.


Long gone are the days were one person would control all the analytics capabilities. Instead, we’ve evolved to the point where IT spending has become more of a collaborative effort where each analyst’s input should be valued and respected as there can be several paths to a solution.

Analytic organizations are coming to a point where community is in charge, not the individual. With scattered sources, educational backgrounds and data perspectives, this means data teams will need to implement internal social networks where collaboration is the more effective method of data analysis, as opposed to disparate ideas taking longer to come together.

Analytics Ecosystems

Analytics EcosystemsOne of the first steps in establishing a comprehensive big data infrastructure entails using the varying inter-dependencies of your company and synchronizing them. Viewing your company’s BI solutions as an ecosystem goes a long way in collaborative efforts to make use of the big data that you collect.

Take it from IBM Data Magazine:

“A successful big data analytics program requires many interacting elements … data, which has to be integrated from many sources, different types of analysis and skills to generate insights and active stakeholders who need to collaborate effectively to act on insights generated.”

As a “network of interconnected and interdependent entities,” having a well-defined ecosystem helps to alleviate the path of big data while making it useful for every element of the system. There are three interacting spheres that define the analytic ecosystem including the core, extended and external.

Data Privacy

The subject of data privacy, while evolving, is still trailing the evolution of our technology. With the necessary involvement of governments and politicians, it’s likely that the laws that need to be stringent will become so far after they’re initially needed.

The private sector also has a responsibility in maintaining transparency with their customers and partners as to where their information is being stored and if it’s being distributed to a third-party.

As more conversations are had surrounding the subject, the coming years should offer more solutions that protect consumers while holding companies and governments accountable for their actions.