On-premise or Onto the Cloud?

Do you have to move your processes to the cloud today? The answer is you don’t. Do it when you are ready and when you see value for your organization. If, and when you’re ready to start your journey into the cloud, you can go about it piece by piece, and as a hybrid, to minimize risk to your core operations and to get buy-in internally.

Perhaps you could begin with applications that bring tremendous value, yet are non-disruptive such as workforce planning and analytics? Your newspaper, music, pictures and videos are moving into the cloud, your bills and banking are moving to the cloud, you telecommute, and your social is moving into the cloud - it’s inevitable that your business applications, including HR will follow.

What Is Mobile Business Intelligence?

You might have heard this statistic by now: more people own a cell phone than a toothbrush. In a Forbes post, Maribel Lopez lists a number of recent statistics about mobility. “While we could debate the numbers, the trend is clear,” she writes. ”The pace of mobile adoption across devices and applications is accelerating.”

Mobility is no longer a nice-to-have option. Instead, it’s become a must for many businesses.

Ten Questions To Develop Your Mobile Business Intelligence Strategy

In my post “Mobile BI” Doesn’t Mean “Mobile-Enabled Reports” I articulated the importance of developing a mobile business intelligence (BI) strategy. If designed, implemented, and executed effectively, mobile BI will not only complement the existing business intelligence framework, but it will enable organizations to drive growth and profitability.

For the next ten weeks, I want to chart a course that will highlight the key questions you need to ask before embarking on a mobile BI journey. This is the critical first step in validating mobile BI readiness for any organization, whether it’s a Fortune 500 company, a small-to-medium enterprise, or a small team within a large enterprise.

Three Strategies To Get Started With Mobile Business Intelligence

A “mobile-only” strategy reflects a strong commitment, or all-in approach, by the management team to mobile BI, or mobility in general. This may be due to a specific reason, such as the relevance of mobility in a particular industry or the opportunity to create a strategic advantage in a highly competitive market. Or a company may decide that mobility needs to be a vital part of their vision.

However, in order for this strategy to be successful, it requires a commitment that results in both championing the cause at the board or senior management level and making the necessary resources available for execution at the tactical level.

In reality, this approach doesn’t necessarily translate into creating a mobile version of every analysis or shutting down all production lines for PC-based outlets for reporting and analytics. Instead, it reflects a strong emphasis on establishing scalable mobile consumption paths for analytics, and it signals a willingness to exploit a mobile-first mindset.

Analytics – from the World of Finance to the HR Organization

In the finance and banking industries, organizations have employed for years mathematicians, statisticians, engineers, physicists, and highly-skilled specialists with super-strong analytical skills. They put these skills to work, sifting through volumes of financial, economic, and social data to identify trends, pick out the “needles in the haystack,” and determine the probability of markets going up or down. Their brain power, combined with machine resources, is focused keenly on exploring and acting on new ideas to increase the return on investments, whether through gaining a sub-second advantage in trading or in long-term ventures.

However, the idea of tapping big data in the context of the workforce, in order to gain a competitive edge, is just beginning to sink in with many HR organizations.

4 Tips on Creating Effective BI Teams

Thanks to software vendors investing heavily in making their applications easier-to-use, accessible via the Web and more affordable, Business intelligence (BI) software is now a viable option for employees in sales, marketing, operations, and other departments to utilize.

But with the proliferation of these tools, business leaders will have to rethink how they address business intelligence governance, or the roles, responsibilities and guidelines it provides its users to ensure BI tools are utilized correctly and appropriately. When it comes to setting up these new BI teams for success, leaders should consider these three steps.

The Big Data Revolution: Part 2

Starting from the current obsession with datafication – “taking information about all things under the sun…and transforming it into a data format to make it quantified” – Big Data identifies three major developments, incredibly large data sets, acceptance of messy data, and a tolerance for correlation in lieu of causation, as drivers of the revolution. From Big Data’s perspective, the business and social implications of these shifts are substantial.

The value of data is evolving.

Making Certain You Get the Most from Deployments

It has been said that it is very difficult to determine ROI for Business Intelligence software. In a blog entry on B-Eye-Network by Wayne Eckerson, How to Measure BI Success, he mentions many types of measurements. It is always important to measure the performance of your Business Intelligence software to make sure that there is continuous value. What about those organizations who avoid a BI software purchase due to cost concerns and the expectation that the software will not fulfill its promises.

Why not try before you buy?

What is Dirty Data Costing You?

Intrinsically, you know that you need good data. But how far do you need to go? What are the real costs incurred if you DON’T have clean data?

The most common data quality question is, “We’re still making money, so how bad can it really be?”

Consider the following scenario. You made the “mistake” of hiring good people. Rock stars, even. They’re smart people, and they want to make sure you keep making money. As processes break and bad reports are generated, they want to fix them. So they do. Herein lies the “mistake.” Now, all kinds of shadow, manual processes are happening in your company to make the data fit-for-use.

This is how data quality affects labor productivity. When data is incorrect, bad things happen. Bills don’t get paid on time, shipments get returned, and so on. That’s not the end of the story, either. Now an employee has to step in and start a new process—for example, one that handles return shipments. When data quality is improved, however, much of this work can be avoided.