Human Resources is beginning to pull ahead in the Business Analytics world. One thing is certain, if you want to run a successful business, you need to hire the right people to help you get there. Who is in charge of recruiting and hiring your team members? “DING, DING, DING!” you guessed it, Human Resources. In a “Big Data” world, HR can use “People Data” to their advantage and help businesses develop strategy when it comes to hiring the best candidates. As David Klobucher writes in his article, “Data-driven confidence will help HR professionals identify behaviors and interview styles that attract better employees, as well as qualities that make effective workers – and lead to faster promotions.”
I agree with Klobucher, this is a great time to be in HR. There are big opportunities that may be presented to anyone working in HR. Executives within businesses are looking to their Human Resources department to help build the strategy to success. Of course this all depends on if HR professionals “welcome” the business analytics technology with warm arms. As stated in the article, many individuals working with Human Resources are not completely comfortable using data just yet. In today’s world, Big Data surrounds all of us, but for HR, this can lead to big success from analyzing data of past successes and past failures.
In some HR departments, to take on this scope of technology could be intimidating, however like one of my favorite sayings goes, “I never said it would be easy, I only said it would be worth it.”
Read original article here.
Originally posted on October 26, 2015.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- The Only Constant? Change: Every thing is constantly changing and evolving so this will continue to test your BI deployment at all times.
- 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.
- 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.
- 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.
- 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.
Let’s take a look into some reasons that business intelligence projects tend to fail.
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.