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.
Profitability relies on productivity. In today’s high-speed world, everyone is looking for ways to do more in less time, and it’s only natural to seek technological assistance. Artificial intelligence (AI) has rapidly gained adoption across industries, and its benefits for businesses have not gone unnoticed. While companies of all sizes can benefit from AI’s productivity-improving capabilities, we’ve chosen to highlight some of the greatest impacts this technology can have on your workforce and your business.
By 2035, AI may have increased workforce productivity by at least 40 percent, and this could potentially boost profitability an average of 38 percent. AI-driven innovations in cloud computing, big data storage, and analytics have made notable strides in improving employee productivity, manufacturing efficiency and overall better business performance.
AI enables employees to have a more productive workday, and that begins with the alleviation of particularly tedious and mundane workloads. Machines never need breaks and can repeat the same task 24 hours a day without losing energy or focus, allowing traditionally time-consuming work to be accomplished efficiently without the need for human labor. As technology advances and more responsibilities are covered autonomously, we can expect AI to “create new lines of productivity, new and better jobs, new professions and untold wealth,” as AI enthusiast Mark Hurd paraphrases from tech scholar Melvin Kranzberg.
One of AI’s most beneficial qualities is its ability to gather, store and analyze vast amounts of data. As business success now relies heavily on obtaining and utilizing qualified data, AI’s data analytics functions can be incredibly valuable to productivity. Employees no longer need to manually gather and organize data, because AI can collect at a superhuman rate. From this data, AI provides companies with both prescriptive and predictive insights, giving businesses a look at potential future opportunities while offering recommendations on how to take action. These insights can be utilized in many different productivity-boosting ways.
Predictive analytics uses statistical models, machine learning and forecasting techniques to anticipate a company’s future and provide intelligent answers to the simple question of “what could happen?” These insights can be used throughout an organization to forecast customer behavior and purchase patterns, identify trends in sales activities and forecast demand for inputs from the supply chain, operations, and inventory. AI-driven predictive maintenance solutions, which track the conditions of equipment in manufacturing facilities and analyze data on an ongoing basis, enable organizations to service equipment when they need it rather than at scheduled service times. These implementations can cut downtime by as much as 20 percent and can also produce a 10 percent reduction in annual maintenance costs as well as a 25 percent reduction in inspection costs.
By utilizing a combination of algorithms and machine learning, prescriptive analytics can play a major role in how businesses make decisions. It’s best used for optimizing production plans, scheduling and monitoring inventory in supply chains. Staying on top of these complex processes and anticipating problems in advance can ensure the right services are provided at the right time for optimal customer experiences.
Positive customer experiences have always been important, but as technology becomes more accessible, brands are held to higher standards. Now more than ever, the need for optimized customer service is vital. Because most companies don’t have the resources for around-the-clock customer service team, AI can have a considerable impact on service quality and on the speed at which businesses react to customer issues.
Basic requests and frequently asked questions can bog down employees. AI-powered chatbots help to refine companies’ customer service offerings by handling these low-level interactions. Chatbots are also better than human agents at consistently collecting data from their interactions. Businesses can use this data to, among other things, analyze customer intent from past queries and automatically route requests to the proper person. This allows customer service teams to focus their time and efforts where they can be most productive and produce the greatest value.
Though many worry that AI will diminish the value of human work, humans will still be needed for 80 percent of complex problem-solving tasks in the future. When AI takes over mundane responsibilities, employees can show greater value through more productive, meaningful work. This, in turn, leads to better positions, which may involve performing the kinds of jobs that have yet to be invented. This evolution can also increase productivity, as employees will have more time to work on tasks they enjoy and can take pride in.
Artificial intelligence can also serve as a great tool for evaluating potential candidates for open positions. This takes some the burden off of HR staff, allowing them to focus on vetting great prospects and rewarding top employees.AI can also measure the productivity of current employees to analyze what’s working and what’s not. Through this, businesses can adjust their methods as needed to increase production and to provide qualified feedback to improve employee confidence and encourage loyalty and skill development.
80 percent of executives believe that AI can significantly improve productivity for their companies. The power AI displays today, and the potential it has in the future, will prove greatly beneficial to businesses of all industries.
Data Virtualization is a process that gathers and integrates data from multiple sources, locations, and formats to create a single stream of data without any overlap or redundancy.
Data Virtualization innovation is helpful, in our world of non-stop data transmission and high-speed information sharing, as a tool to aid in collecting, combining, and curating massive amounts of data.
With big data analytics, companies can locate revenue streams from existing data in storage, or they can find ways to reduce costs through efficiency. However, this is easier said than done. IT companies generally have multiple, dissimilar sources of information, so accessing that data can be time consuming and difficult. Data virtualization systems can help.
Companies that have implemented data virtualization software have better, quicker integration speeds and can improve and quicken their decision-making.
What is Data Virtualization
Data virtualization (DV) creates one “virtual” layer of data that distributes unified data services across multiple users and applications. This gives users quicker access to all data, cuts down on replication, reduces costs, and provides data flexible to change.
Though it performs like traditional data integration, DV uses modern technology to bring real-time data integration together for less money and more flexibility. DV has the ability to replace current forms of data integration and lessens the need for replicated data marts and data warehouses.
Data virtualization can seamlessly function between derived data resources and original data resources, whether from an onsite server farm or a cloud-based storage facility. This allows businesses to bring their data together quickly and cleanly.
How Virtualization Works
Most people who use IT are familiar with the concept of data virtualization. Let’s say you store photos on Facebook. When you upload a picture from your personal computer, you provide the upload tool with the photo’s file path.
After you upload to Facebook, however, you can get the photo back without knowing its new file path. Facebook has an abstraction layer of DV that secures technical information. This layer is what is meant by data virtualization.
When a company wants to build Virtual Data Services, there are three steps to follow:
- Connect & Virtualize Any Source: Quickly access disparate structured and unstructured data sources using connectors. Bring the metadata on board and create as normal source views in the DV layer.
- Combine & Integrate into Business Data Views: Integrate and transform source views into typical business views of data. This can be achieved in a GUI or scripted environment.
- Publish & Secure Data Services: Turn any virtual data views into SQL views or a dozen other data formats.
Once a DV environment is in place, users will be able to accomplish tasks using integrated information. A DV environment allows for the search and discovery of information from varied streams.
- Global Metadata: Global information search capability lets users access data through any format from anywhere in the world.
- Hybrid Query Optimization: Allows for the optimization of queries, even with “on-demand pull and scheduled batch push data requests.”
- Integrated Business Information: Data virtualization brings users integrated information while hiding the complexity of accessing varied data streams.
- Data Governance: DV layer serves as a unified layer to present business metadata to users. Simultaneously, it helps to understand the underlying data layers through data profiling, data lineage, change impact analysis and other tools and expose needs for data normalization / quality in underlying sources.
- Security and Service Level Policy: All integrated DV data views can be secured and authenticated to users, roles and groups. Additional security and access policies can manage service levels to avoid system overuse.
Data Virtualization Tools
The various capabilities that Data Virtualization delivers offers companies a newer, faster method of obtaining and integrating information from multiple sources. The top tools currently in use are as follows:
- Logical abstraction and decoupling
- Enhanced data federation
- Semantic integration of structured & unstructured data
- Agile data services provisioning
- Unified data governance & security
These capabilities cannot be found organized in any other integration middleware. While IT specialists can custom code them, that minimizes the agility and speed advantages DV offers.
Data Virtualization creates many benefits for the companies using it:
- Quickly combine multiple data sources as query-able services
- Improve productivity in IT and by business data users (50%-90%)
- Accelerate time-to-value
- Improve quality and eliminate latency of data
- Remove the costs associated with populating and maintaining a Data Warehouse
- Significantly reduce the need for multiple copies of any data
- Less hardware infrastructure
While this innovate new path to data collection and storage offers increased speed and agility, it is important to note what DV is not meant to be.
What Data Virtualization is Not
In the business world, particularly in IT, there are buzzwords flying about in marketing strategies and among industry analysts. It is therefore important to make note of what Data Virtualization is not:
- Data visualization: Though it seems similar, visualization is the physical display of data to users graphically. Data virtualization is middleware that streamlines the search and collection of data.
- A replicated data store: Data virtualization does not copy information to itself. It only stores metadata for virtual views and integration logic.
- A Logical Data Warehouse: Logical DWH is an architecture, not a platform. Data Virtualization is technology used in “creating a logical DWH by combining multiple data sources, data warehouses and big data stores.”
- Data federation: Data virtualization is a superset of capabilities that includes advanced data federation.
- Virtualized data storage: VDS is database and storage hardware; it does not offer real-time data integration or services across multiple platforms.
- Virtualization: When used alone, the term “virtualization” refers to hardware virtualization — servers, networks, storage disks, etc.
Myths and Inaccuracies
As with every new innovation in technology, there will always be myths and inaccuracies surrounding implementation.
We don’t need to virtualize our data – we already have a data warehouse.
The sources of unstructured data increase every day. You can still use your data warehouse, but virtualization allows you to tie in these new sources of data to produce better information and a competitive advantage for your business.
Implementing new data technology isn’t cost effective.
Data virtualization software costs are comparable to building a custom data center. DV also does not require as many IT specialists to use and maintain the system.
Querying virtual data can’t perform like physical data queries.
With the constant innovation and improvement of computing platforms, faster network connections, processor improvements, and new memory storage, virtualization software can process queries with multiple unconnected data sources at near real-time speeds.
Data virtualization is too complex.
When something is new in technology, humans have the tendency to question it based on their own lack of experience. Most virtualized software is easy enough to be used by geeks and laymen alike.
The purpose of data virtualization is to emulate a virtual data warehouse.
While DV can work this way, it is more valuable when data marts are connected to data warehouses to supplement them. “The flexibility of data virtualization allows you to customize a data structure that fits your business without completely disrupting your current data solution.”
Data virtualization and data federation are the same thing.
Data federation is just one piece of the full data virtualization picture. Data federation can standardize data stored on different servers, in various access languages, or with dissimilar APIs. This standardizing capability allows for the successful mining of data from multiple sources and the maximizing of data integration.
Data virtualization only provides limited data cleansing because of real-time conversion.
This is a claim that can be made about any number of data query software programs. It is best to clean up system data natively rather than burden query software with transformation of data.
Data virtualization requires shared storage.
Data virtualization is quite versatile. It allows you to build customized storage devices for your system needs.
Data virtualization can’t perform as fast as ETL.
Through data reduction, data virtualization performs more quickly than ETL. “Operations perform at higher speeds because the raw data is presented in a more concise method due to compression, algorithmic selection and redundancy elimination.”
Data virtualization can’t provide real-time data.
DV sources are updated live instead of providing snapshot data, which is often out of date. “It is closer to providing real-time data and faster than other data types that have to maintain persistent connections.”
Why Do We Need Virtualization?
Data is transferred among users in different speeds, formats, and methods. These variables make Data Virtualization a must have in the global business world. DV will help companies search, collect, and integrate information from various users, platforms, and storage hubs much more quickly. This will save the company time and money.
Data Virtualization is perfect when data demands change on the fly and when access to real-time data is critical to positive business outcomes. DV also provides you with access to any data storage system you are currently using. Despite the differences in storage platforms and systems, DV will allow you to integrate all the material in a single model.
Data Virtualization offers help in security challenges because the data is not transferred – it is left at the source as DV provides virtual access from anywhere. This is also cost-effective as you will not be duplicating any data.
As we move further into the technical age of global systems, the need for Data Virtualization becomes clear. Access to information across platforms, languages, and storage types will precipitate a faster and more useful transfer of data that everyone can use.
The future is here. The future is now.
Big data is a modern technology that captures huge amounts of data, which is then analyzed in order to reveal patterns and trends. Organizations need to transform what they have gathered into business insights that promote growth.
Across business in general, there are 5 sectors that are intelligently using big data to improve their operations and connect with customers better.
By using big data, fashion industry moguls can predict when there is a market for businesses to carry certain products. Big data gives them the opportunity to stop the manufacturing of some items, and focus on the products that sell well.
According to researchers, big data presents fashion companies with opportunities to engage businesses and markets through effective content on social media. The leading fashion brands use comments on social media and turn them into engaging conversations, thereby increasing their global presence and at the same time gathering necessary data for future analysis of patterns and trends. Fashion designers and magazine publishers are constantly producing innovative fashion content to attract customers. This allows them to collect the data received from the different content outlets and see how the fashion industry is changing.
If marketing-based businesses are driven by analytics, then market intelligence groups are the engines of it all. Big data and analytics are on a whole new level today, creating vast new opportunities for company leaders. The last few years have seen a huge increase in the quantity of data available from different sources including mobile phones, social media, and transactional data from people’s online behavior. Only businesses that are based on good research can minimize operational risks, as well as pursue profitable opportunities for growth. Fortunately, market intelligence groups now have the tools, approaches, and talent, in order to turn gathered data into a competitive advantage. Market intelligence can either make businesses realize their potential or advise them against unrealistic expectations.
Data is the basis of any customer intelligence group. Today’s businesses are generating huge amounts of data through several channels, which include online stores, kiosks, sales offices, and even call centers. In addition, organizations are now using customer and prospect data in order to generate information through reseller and advertising networks. This year, customer intelligence organizations can gain insights by integrating views of the aforementioned sources with different target markets. Customer intelligence groups are now developing strategies for accessing and integrating customer data into making big business decisions.
Read more about how Marketing Intelligence and Customer Intelligence provide powerful ways to strategize, ensure customer satisfaction, and continued growth
Bloomberg claims that the global financial industry has evolved enormously over the past decade in terms of investments and trading. The sheer volume of financial data is constantly growing, which has a direct impact on the value of different assets that can be traded online. These assets include currencies, cryptocurrencies, commodities, indices, stocks, and options. The information contained in big data can greatly improve the way investment and trade indicators are gathered and used.
Big data has been a crucial step for advancements in online trading. Without big data, online trading platforms are unable to create solutions that would ease the everyday activities of traders. Nadex states that people can now perform transactions on a regulated US-based exchange, as well as trade on different markets using just one account on a computing device. It’s thanks to big data that the financial industry can expand their solutions, as well as improve their offerings based on their customers’ needs. Big data is a huge step towards the development of investment and trade, and it will further propel the industry towards positive changes this year.
Big data integration is the combination of technology and business procedures used for synthesizing data from dissimilar sources. A complete data integration solution must deliver trusted data from different sources.
With a high volume of sensor and social data being generated every day, it is now critical for data scientists to make large amounts of information available for analytics and consumption. Data must not only be gathered, but its accuracy must also be assured. The powerful and far-reaching data integration of several digital frameworks will help this idea materialize in 2018.
Businesses are becoming more digital than ever which means data are being gathered by organizations in gigabytes. Utilizing data can provide leaders with unexpected information that lead to unconventional yet successful business strategies. While some industries are already taking advantage of the insights that big data provide, we can only expect more and more enterprises to join the data revolution as businesses become more competitive.
Not too long ago businesses found it very difficult to query data out of their recently acquired relational databases. These queries were too slow to be processed by computer systems of the time as well as too inflexible to navigate the data. After trying different solutions offered by various big corporations in the market, OLAP came into being.
One of the primary goals that OLAP vendors try to achieve is to minimize the amount of on the fly processing needed when the user was simultaneously navigating the data. This was achieved by pre-processing and storing every possible combination of dimensions, measures, and hierarchies before the user started their analysis.
The earlier versions of the OLAP cube provided a snapshot of data at a specific point in time. OLAP pre-computes all the totals and sub-totals that need to be reported at a time the servers are usually idle. This allowed data to appear at the same time as it was being investigated by the user.
Because OLAP cubes pre-calculate all the resulting combinations between dimensions, you can do some amazing analysis. For instance, all at once, you can analyze sales by region, by product type, by period of time, by store, by sales representative, and by budget. Crazy, right? It gets so overwhelming that sometimes you may have to back up and try to figure out what kind of analysis you are trying to do. With practice, OLAP cubes speeds up the data modelling and analysis process by significantly minimizing manual operations.
Here are some interesting facts about the history of OLAP technology:
- Edgar Codd, the person who designed the OLAP cube and coined the term OLAP (Online Analytical Processing), he is a veteran in the field.
- SEQUEL (Structured English Query Language) was actually the first version of SQL (Structured Query Language).
- ‘Slice’ among OLAP technicians means dividing any cube shaped item into two.
- The OLAP cube is made up of dimensions, measures, and hierarchies.
- Arrangement of data into a cube is what allows large amounts of data to be analyzed as it is displayed instantaneously.
- The query language used to interact and perform tasks using an OLAP cube is called multidimensional expressions (MDX). It was first developed by Microsoft in 1990s and then got taken up by numerous other vendors in the market.
- OLAP cubes are designed for business users and therefore use business logic and understanding. However, business users can query OLAP cubes using Standard English.
Since its early beginning, OLAP technology has evolved together with advances in processing, connectivity, and cloud services. For instance, modern OLAP products are not limited by just calculation of aggregates. It can now calculate custom formulas and run driver-based computations which are useful for financial reporting, planning, forecasting, and predictive analytics.
Modern OLAP are also more dynamic. By adding connectivity to data sources, OLAP now provides an up-to-date view of a business – even computing on a real-time basis. It is important to note also that some OLAP are available on the cloud as a service, as more companies move toward a virtualized hosted environment.
Interested in trying an OLAP service in the cloud? Check this out.
OLAP technology falls under the umbrella term “Business Intelligence” which is composed of various tools such as data visualization, and more. BI capabilities are being packaged together with business solutions to provide an analytical perspective of the business.
For example, some eCommerce tools such as those offered by Magento development companies incorporate BI capabilities. You can hire Magento developers and see how the integration of these tools improves the outcomes of the business.
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.
Business Analytics vs. Data Science
“Business analytics” and “data science” — are they basically interchangeable terms, or entirely separate professional pursuits? There’s certainly overlap on the topic of Big Data and using data to inform decisions. There is no dispute over the fact that both business analysts and data scientists use exponentially growing sources of data to do their work. [Check out PARIS Tech’s recent post on Big Data]
An article and featured infographic by Angela Guess for Dataversity.net argues that the terms business intelligence and data scientist are distinct, and not just because one pursuit applies to business, and the other to scientific results.
Click below to read the original article which accompanies the business intelligence vs. data scientist infographic.
Infographic: Business Analytics v. Data Science
What is Business Analytics?
Science and business continue to intersect, most recently on the topic of data analytics. Generally speaking, “data analytics” is the process of organizing and interpreting data to uncover valuable information. “Business [data] analytics” is the more specific application of data analytics to business purposes.
Some examples of data analytics might be: What segment of customers use desktop v. mobile? Or, which target audience found value in the most recent advertising campaign? Companies ranging from Target to Google find results from these kinds of questions so valuable that they pay data analysts over $100,000 per year. To learn more about the burgeoning data analytics industry, check out this educational resource, created by Villanova University’s Master of Science in Analytics program.
OLAP and Hadoop: A Great Pairing
OLAP continues to be a relevant and exciting technology, most recently in pairing OLAP and Hadoop. As we are OLAP.com, we have ALWAYS seen the value of OLAP technology. We admit OLAP has been a bit out of style the last few years. Some companies even run Google ads about how “OLAP is obsolete,” but nothing could be further from the truth. (Check out our blog on that one.)
We see this in the fashion industry all the time: what is old is new again! This is rare in the technology realm, but it seems to be the case with OLAP. As developers struggle to get value out of Hadoop data, they discovered they needed the speed and flexibility of OLAP. OLAP and Hadoop is a powerful combination for getting to the ultimate goal of extracting value from Big Data.
Bringing OLAP to scale for Big Data
In an article from ZDNet, Is this the age of Big OLAP? Andrew Brust writes about the new relationship between OLAP and Hadoop. He highlights that OLAP technology can be particularly beneficial when working with extremely large Big Data sets. Typically, OLAP has not been scalable enough for Big Data solutions. But OLAP technology continues to progress, we find this new application of OLAP exciting. Brust discusses a few strategies for bringing the two technologies together. He mentions a few OLAP vendors in detail and how they manage the issue of scalability for OLAP software.
If you want to try using OLAP with Hadoop, perhaps you want to give PowerOLAP, the mature OLAP product of OLAP.com, a try? There is a free version of PowerOLAP available. If you plan to test PowerOLAP with your Hadoop, contact PARIS Tech, and they will lift the member limit for you in the free version, as you will need to go beyond the member limit that ships with the free version.
In sum, OLAP.com is pleased to see OLAP rising in relevance once again and getting some of the recognition we felt it deserved all along. It is a testament to the power and value OLAP has as a technology.