Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS).
Based on that definition of Business Intelligence, we can say that Predictive Analytics actually falls under the umbrella of BI. How else could there be a decision support system without considering future plans and forecasts?
Instead of comparing Predictive Analytics with BI, it makes more sense to differentiate it with Descriptive Analytics (what traditional BI tools offer).
According to The Institute of Business Forecasting and Planning (“IBF”), “It is important to understand that all levels of analytics provide value whether it is descriptive or predictive, and all are used in different applications.”
Below are excerpts from IBF’s blog:
Descriptive Analytics: Data, data, data
The easiest way to define it is the process of gathering and interpreting data to describe what has occurred.
Descriptive analytics takes the raw data and, through data aggregation or data mining, provides valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why.
Predictive Analytics: The Future
Predictive analytics, no longer asks what happened, but why it happened, and what could happen in the future.
It brings together a number of data mining methodologies, forecasting methods, predictive models and analytical techniques to analyze current data, assess risk and opportunities, and capture relationships and make predictions about the future. At this stage you are no longer just asking what happened, but why it happened, and what could happen in the future.
Read full post:
The Differences Between Descriptive, Diagnostic, Predictive & Cognitive Analytics
Check out this cool infographic! Submitted to OLAP.com by the folks at Business Intelligence Technologies. They’ve mapped here 5 warning signs that you and your professional team are living in Excel Hell. Perhaps it’s just been accepted that your Excel Hell is what it is, but we beg to differ. Professional teams overuse Excel, to a point way beyond what it was designed to do as a personal productivity tool. This fact has led to many technology and BI software vendors positioning themselves against Excel as if it is something to be removed. Sacrilege, cry the users! And us too! People responsible for planning in particular, need the flexibility that Excel provides and find almost any other tool too rigid. (See the Wall Street Journal article: Finance Pros Say You’ll Have to Pry Excel Out of Their Cold, Dead Hands) So, if Excel is essential, how do we get free from Excel Hell?
For further reading, see the blog on this topic on the Business Intelligence Technologies website.
By Jack Guarneri
In the far distant past (OK, just a few years ago) prospects would balk at the idea of EPM in the Cloud. Putting key company information, like the kind contained in an EPM solution, into the Cloud was considered too risky. Things sure have changed: the Cloud, as a means to do everything—from data storage, to playing music, to business application hosting—is as much a part of our technology ecosystem as the phones in our hands and the laptops on our desks. Along with this, firms have become dramatically more willing to use a subscription model for the purchase and management of (cloud) solutions. Indeed, “cloud-based” and “subscription service” have come to mean pretty much the same thing in respect to business applications.
Here’s some hard evidence concerning how in-the-cloud business systems have gained acceptance: Dynamics 365, Microsoft’s cloud-based enterprise resource planning (ERP) and customer relationship management (CRM) applications, grew by 61 percent year over year in Microsoft’s Q4.
And yet…there seems to be a lagging sector of the market, where the Cloud is not so prominent: applications related to Enterprise Performance Management (EPM). That’s the umbrella term for mission-critical, collaborative solutions for budget planning, forecasting, consolidations, “what if” analyses—in sum, precisely the kinds of things that firms around the world mash-up in massive and ghastly spreadsheet-only models.
It’s this very fact—“the Excel-ness” of these tasks—that may hold the key for explaining why the Cloud hasn’t caught up. It’s also a great argument for why a cloud-based subscription service makes the most sense for the EPM market, like what Business Intelligence Technologies offers.
Let’s first briefly examine why “EPM in the Cloud” makes great sense. These are arguments for cloud-based services for all kinds of business applications—but we’ll touch on their relevance to EPM-in-the-cloud in particular:
- Lower cost – risk mitigation — By definition, a subscription service concerns payment spread over time, rather than—with a typical on-premise system—made entirely up front; you might even get an offer to obtain consulting services baked into the fixed monthly cost. And though you are likely be required to sign up for, say, 12 months minimum, if things go south, you won’t be tied to a fully purchased albatross. Looking at it more optimistically: for EPM cloud solutions, you’ll learn, at a minimum, to get an expert’s insight on at least one component of your EPM eco-system, which you can build on by adding complexity, often an enticing incremental cost.
- Security — If any kind of business application is crying out for a centralized, secure database—almost certainly more secure in the Cloud than on laptops or your LAN—it’s an EPM system. Businesses now understand that they stand to gain by losing the thousands of disparate files, so often emailed, frequently out of sync and always at risk.
- Maintenance/Efficiencies — owners of on-premise solutions are loath to upgrade, and for good reason: it’s a pain, and it can be costly to “own”; in the Cloud, your service provider will handle this for you. And whether it’s an end to managing software, or corralling spreadsheets, you get to free yourself from desktop data silos, obtaining resource-hours for analytical purposes, which are the true objective of your EPM system
But that Nagging Excel-ness…and the DIY Instinct for EPM Solutions
And yet, even with those arguments, we have the seeming mystery—why is the Cloud not as popular for EPM as it is, say, for ERP?
Here again, as so often is the case with EPM, we can likely look to Excel to lay the blame. For it is the nature of the inveterate spreadsheet user—and the more expert, the more it’s in his or her nature—to want to “do it yourself” in Excel. Insofar as EPM applications are concerned, it’s more likely than not that a spreadsheet jockey will “set up a new tab,” “create an input template” or “jigger with these VLOOKUPS” to add to what is already a cobbled-together solution.
EPM solutions are composed of what we might describe as an ongoing series of spreadsheet-like complexities—involving drivers, user inputs, premises, calculations—all the kinds of things that attract the DIY instinct of spreadsheet pros. Contrast that with ERP , a business process system concerned with recording transactions at the most detail level of the business. An EPM application, on the other hand, concerns higher-order outputs: planning, analytics, non-standard reporting.
The “resistance”—and that probably is a fair way of putting it—to EPM in the Cloud is the resistance of users to abandon a DIY mindset of solving problems with that trusty, and often the only, tool at hand: Excel.
Happily, for Excel users, there’s a way forward—solutions that embrace both Excel and the Cloud.
And for firms thinking about the potentialities of the Cloud as a viable solution for their EPM requirements, that should be the first order of business: finding vendors with the best suite of front-end tools—with Excel at the very top of the list—not just to overcome user resistance, but also to incorporate all their “spreadsheet smarts” in a top-flight EPM solution.
7 Key Business Intelligence Software Trends for 2019
By Keith Craig, Better Buys
Peter Drucker, father of the Knowledge Economy and business management guru said, “Knowledge has to be improved, challenged and increased constantly, or it vanishes.”
Nowadays, vanishing isn’t the worry. Rather, that knowledge – in the form of raw data – has been constantly and exponentially increasing. Data sources are myriad and everywhere.
Have a doctor’s appointment? Your vitals, diagnosis, and Rx get databased. Engage an e-commerce website? Your keystrokes and submitted information get funneled to a CRM. Run a factory? Smart machines record their performance metrics. Involved in a supply chain? Data on product distribution and raw material use gets monitored and stored for future reference.
With this ever-increasing aggregation of factual data, software platforms – many utilizing Online Analytical Processing (OLAP) technology – facilitate ad-hoc analysis across multiple dimensions. Once the data has been stored, BI software slices, dices, and juliennes it. Visualizations yield insight through charts and graphs that populate dashboards. Such business intelligence software delivers value by generating real-time analytics that delineate trends, from which company principals can confidently make proactive decisions rooted in facts.
The impact to your business? Decisions rendered from Business Intelligence improve personnel, product and user experiences. Your company runs better. Staff is content and productive. Customers are happy. Product moves. Revenue climbs. Profits soar.
Drucker would be thrilled with today’s Business Intelligence software, which by its very nature improves and challenges marketplace and workplace knowledge. He would find it unsurprising that the trend to use Business Intelligence software continues to surge.
The following infographic on 7 Key Trends reflects this sustained momentum, popularity, and utility of Business Intelligence software as we move toward 2019.
Keith Craig is Content Marketing Manager for Better Buys. He has more than a decade of experience using, researching and writing about business software and hardware. He can be found on Twitter and LinkedIn.
As the fields of business intelligence and business analytics continue to develop and grow, organizations must be aware of the distinctions between the terms and understand their value. Adoption and usage of business intelligence and analytics tools show no sign of slowing. Understanding these concepts is vital to making the best business decisions, to maintaining a competitive edge across all industries, and to enabling companies to capture operational and strategic value.
To learn more, see the infographic below created by Pepperdine University’s Online MBA program.
Distinguishing Business Analytics and Business Intelligence – Resource from Pepperdine University
Differences Between Business Analytics and Business Intelligence
The goal of business analytics is to develop successful analysis models. It simulates scenarios to predict future conditions. It is a very technical approach to predict upcoming trends. This process helps find patterns after analyzing previous and current data. The analysis is used to devise future courses of action. Professionals working in this field use data mining, descriptive modeling, and simulations.
Business intelligence uses different types of software applications to analyze raw data. Professionals working in this field study business information. They closely consult with decision-making managers. They identify existing business problems and analyze past and present data to determine performance quality. They use KPIs and other metrics, and prepare easy-to-read reports. The reports give unique insights into the workings of the business and empower organizations to make optimum business decisions.
Business analytics experts help predict what is going to happen in the future. They use data to analyze what will happen under certain specific conditions. They can predict the next trends and outcomes.
Business intelligence experts, on the other hand, help track and monitor data and business metrics. They can correctly identify what happened and what is happening now. They can discover why something happened, how many times something happened, and when all such events took place.
Data-Focused Talent Shortage
Very few managers have high expertise in data fields because the use and analysis of big data has emerged only in the last few years. Even new managers and leaders do not have requisite skills to devise data-driven digital strategies. Most organizations need a new kind of talent base that is well versed in the data-driven business landscape. One McKinsey report estimates that by 2018, the US will face a shortage of 140,000–190,000 data science professionals. Even now, companies must pay very high salaries to employ data analysts. Only large companies can afford such professionals.
The Future of Big Data Analytics
While 78% companies agree that big data will impact their business, only 58% think their company is ready to take advantage of all the potential that big data offers. The reason for this is not difficult to ascertain. Companies must use various techniques to capture data, and the data collected must be realized in a specific format. Data analysts must use exacting methods and processes to analyze this data. Capturing and analyzing big data is a complex process and can be handled only by trained data analysts.
Benefits of Business Analytics
Engaging effective business analytics is necessary to make the right business decisions. Managers with proven analytics skills are better able to plan for future projects. The biggest advantage involves forecasting. Analysis of previous and current data helps predict future trends. This information is crucial to the success of a business. A company may have different types of products. It may keep promoting the fast-selling product while another product that is quickly gaining traction may remain under the radar. Only big data analysis can reveal the importance of the latter product. Business analytics is a forward-thinking way to improve operational efficiency. Decisions can be made faster, and it becomes easier to make sense of large volumes of data.
Benefits of Business Intelligence
Business intelligence proves useful in identifying new opportunities. A company can identify a new market that holds important business opportunities. Product pricing can be tweaked to market demands. Business productivity can be improved. Sales and marketing expenses can be optimized. Business intelligence helps predict customer behavior, which proves useful in improving customer service.
Usage and Adoption of Big Data
Even when the benefits are well known, very few companies are able to use big data analysis in a significant way. Almost 50% of businesses face difficulty in the field of business analytics. They are unable to ensure the quality of data. Without the right talent to manage and analyze data, they are at a disadvantage in the market. Many businesses rely on simple applications to analyze data. These tools are not very effective in analyzing big data. This type of data must be analyzed scientifically. It is a complex job that can be handled only by professionals who possess training and skills in data analytics.
Developing a Big Data Analytics Culture
All types of businesses are working continuously to take advantage of big data. They are using simple as well as complex solutions to work with such data. There is a consensus realization that a high level of data analytics is necessary to ensure business success in today’s market. Now, companies are incorporating data analytics into all their departments. They are using sophisticated tools and solutions to predict future trends. Almost 82% of business executives now take advantage of data-driven reports and dashboards.
Sources of Big Data
Big data is obtained from a wide range of sources. Sales records and financial transactions generate a great volume of useful data. They help devise pricing models for different types of products. The customer database is a key source of data. Large amounts of contact details and other data can be mined from emails, productivity and communication applications. In fact, every business process generates data. All such data must be collected and stored properly.
Businesses need the services of both business analytics and business intelligence experts. There are differences in their positions, but both groups play important roles in the success of a business. As more and more businesses rely on digital strategies, they have to analyze their big data properly and effectively. They need the support of trained and skilled data analysts to help achieve the best business success possible.
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.