To identify threats and opportunities, analysts may look through thousands of data records manually, or define KPIs and make a discovery literally in a few clicks. Which approach will your business choose?
According to Richard Branson, a business magnate and investor, “business opportunities are like buses, there is always another one coming.” The idea seems convincing: however, there is hardly a person who did not feel disappointed when they missed their bus. Likewise, companies prefer not to miss their opportunities. But how to recognize them well in advance?
In fact, a company can identify threats and opportunities with the help of business intelligence. Here, we will not focus on the simplest, but highly inefficient approach of scrolling through thousands of data records. Instead, we will dwell on the approach of defining relevant KPIs, which BI consulting practitioners advise.
As the challenge described is not industry-specific, let’s consider a large product portfolio (100+) – an example relevant to several industries (for example, retail and manufacturing). Now, let’s take a closer look at how business intelligence and data analysis can help in defining KPI metrics and in finding opportunities and threats related to a particular product.
Prepare BI infrastructure
As a business has to deal with a big volume of data, usually taken from numerous sources, in order to reach the data, a company needs to implement BI infrastructure. This requires using a tool that is capable of connecting to multiple data sources from which data is combined to create OLAP data models for slicing and dicing. At this stage, to build a required BI infrastructure and ensure data quality, companies may reach out to business intelligence consulting experts.
Start with the right approach to developing KPIs
The next step is to define KPIs. At this stage, it’s crucial to have a clearly defined strategy and know how to translate it into right KPIs to create a hierarchy where lower levels support higher ones. Thanks to historical data analysis and forecasting, business intelligence allows companies to define metrics and set KPI targets, both long-term and short-term.
Track the dynamics
In a constantly changing environment, it is important to keep track of the dynamics. The following KPIs may be useful for this purpose.
1. Absolute figures
With absolute values, it’s possible to look quickly at best (or worst) results in a few clicks. A simple filtering will put the required information to the top. Having right dimensions and measures, a company will easily learn, for example, what product brought highest (or lowest) sales and margin.
2. Relative figures
Let’s imagine that one of the products from the portfolio shows -2% of sales. Undoubtedly, a decline in sales is not what a company is happy to see. But is this decline alarming? To understand that, you need to look at the portfolio in general:
Product 1: -2%
Product 2: -2.5%
Product 3: -3.2%
Product 4: -5.4%, etc.
When compared with others, Product 1 looks the best, while Product 4 looks problematic, as its sales decrease faster. Besides, there is an overall decline. Correspondingly, a company will focus on improving its overall performance.
3. Right time frame
Choosing the wrong period to measure performance may lead to distorted results. For instance, a company takes the period of last 2 weeks when the sales are growing. But if we look at last 10 weeks, we’ll see a decline followed by a slow recovery.
To avoid serious fluctuations that seasonality brings, it’s necessary to define a seasonal coefficient for each month (for example, Jan: 1.0, Feb: 0.98, Mar: 1.0, …, Jun: 2.5, Jul: 3.2, …) and apply it to the values (for instance, sales). This simple measure will help to get season-neutral values.
Compare Target vs. Fact
How can a company know if a 5-percent growth is enough? It depends on what they defined as good. For that, a company should set a target for each product, as some products cannot (or should not) grow while others are expected to do it. A larger company may need to set more sophisticated targets for every product and region combination. For example, Product 1 should grow fast in TX and CA, while product 34 in NY and PA.
To sum it up
To cope with the challenge of identifying threats and opportunities, a company needs KPIs oriented towards finding these valuable insights. Business intelligence can be a helpful tool for defining these KPIs, and an implemented BI solution will allow filtering, grouping or sorting in a few clicks, instead of scrolling through thousands of lines.
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.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Business data and BI software are currently beginning a new phase of evolution. In this new phase, users of business data will be able to collaborate and connect with other colleagues and team members without multiple spreadsheets and laborious processes.
The BI user experiences of past decades are being re-thought and fast collaboration and crossover functionality are the way of the future. Southard Jones, in the May 9th Venture Beat article Blurred lines: Reimagining the user experience for business intelligence, details how companies are developing new ways of delivering business data and what companies will be looking for in the future.
The article is of interest because it discusses that the business intelligence space needs to evolve to meet the needs of modern businesses. Currently, there is little crossover functionality between products, and products are rigidly aligned to arbitrary user “roles” like information consumers vs. producers. But, people are not rigidly defined in their roles; they need to be able to answer questions quickly, using their business data. “Blurring” the line between consumers and producers of information is one example of how business intelligence products need to evolve, because blurring that line will make everyone more productive.
Crossover functionality is another topic this article broaches. To quote Jones, “Ensuring success with BI and analytics also means recognizing that different people prefer different tools.” We whole heartedly agree! People in business should be able to access relevant, informative data quickly, and from whichever tool seems appropriate to them.
His article feels very validating because at least someone in the BI industry sees the status quo is no way to continue. Jones also writes, “The modern business landscape demands a new approach to the user experience. […] And one that allows interoperability between different products. Our work styles have evolved. BI and analytics should do the same.”
One such software, developed by PARIS Technologies, is taking on this new, modern business crossover and collaboration and use of multiple products with their newest product Olation®. With this kind of technology, companies can eliminate inaccurate data and time-consuming processes that stem from data located in various applications, spreadsheets and databases. With Olation, data is centralized in a non-proprietary database and access to that information is permitted simultaneously by multiple users in their different applications. It’s is a game-changer, especially if you’ve been struggling with a typical or limited BI tool. With everyone in the business working from the same source data set, and Olation’s calculation engine doing the formula and calculation work, there is little manual spreadsheet work to be done. Which means analysts can actually answer questions quickly and spend time getting to the meat of data discovery.
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Some vendors have run from the four letters OLAP…and yet, articles continue to be published, and assertions made, that “analytical processing” is entering a new phase. There’s a new acronym in the market, HTAP, which stands for Hybrid Transactional/Analytical Processing. Given the close similarity (two out of four words ain’t bad—and it’s three if we consider the once-upon-a-time category HOLAP), is there a difference? This post from one of our favorite bloggers, Timo Elliott, may help you decide…
What is HTAP?
Read more about PARIS’ take on the HTAP technology on the PARIS Tech Blog