Blog

December 3rd, 2014

BI_Dec2_COften, when companies look to integrate business intelligence processes the first department that systems are applied to is sales. By employing metrics that track sales activity and any sales-related activity, business owners can gain a better picture of overall success. The problem is, it can be tricky to pick which metrics to track. To help, here are five of the most commonly tracked sales metrics.

The sales pipeline

This metric is often employed by businesses to show current sales opportunities and estimate the number of sales or revenue the sales team will bring in over a set period of time, usually a couple of months. When employed correctly, team members are better able to track and remain in control of their sales. Managers can also be assured that targets are more accurately set and reached.

When companies set up their sales pipeline metrics they often set out to measure:

  1. Average time deals remain in the pipeline.
  2. Average percentage of converted leads.
  3. Average worth of every deal.
  4. The number of potential deals in the pipeline.

Overall sales revenue

This metric is often seen to be the most important sales-related metric to implement, largely because it provides managers and owners with a good overview of the health of their company and overall performance. In short, sales revenue allows you to accurately view the profitability of your business, even if your profits aren't presently growing.

Beyond giving a useful whole-business overview, this metric can also uncover exactly how much each sale influences or contributes to the bottom line. This can be calculated by using the standard profit-ratio equation - net income over sales revenue.

Accuracy of forecasts

Any sales manager knows that forecasts are just that, predictions. But, because so much of sales is based on informed speculation it is important to track the overall accuracy of any future forecasts. By doing so, you can uncover gaps in processes and reveal any forecasting tools that need to be improved.

From here, you can track improvements and tweak forecasts to ensure that they become as accurate as possible. After all, if you can show that you are meeting your goals, or are close to meeting them, you can make more reliable decisions and be assured that your company is doing as well as it appears to be.

Win rate

The win rate, also known as the closure rate, is the rate that shows how many opportunities are being translated into closed sales. Because this rate looks at the number of sales, you want it to be as high as possible, especially when you look at the time your sales team puts into closing sales.

While a high rate is preferable, low win rates are also useful largely because they can highlight areas where improvement is needed. For example, if your team has constantly low win rates across the board, then it could signify that there is a need for more training on closing sales, or that sales staff may not be knowledgeable enough about the products or services being offered. A fluctuating rate could show increased industry competitiveness and highlight when a sales push could be beneficial.

Loss rate

The loss rate can be just as important as the win rate, largely because it focuses on how many potential customers did not purchase products and/or services from you. It can really highlight problematic areas in the early sales process. For example, by tracking the loss rate you may be able to see that response time is low, causing potential customers to walk away.

Essentially, when measured correctly, you can use loss rate to improve the overall sales process and hopefully bump up your overall win rate. You can also compare the two rates to really see how big of a gap there is and give your team a solid goal to try and find ways to reduce this gap.

If you are looking for solutions that allow you to track and measure your sales and any other data you generate, contact us today to learn how we can help turn your data into valuable, viable business information to lead your company to better success.

Published with permission from TechAdvisory.org. Source.

November 6th, 2014

BI_Nov03_CBusinesses who are looking to increase or encourage customer and employee interaction, while simultaneously boosting the quality and amount of data collected, have a number of options at their disposal. One of the increasingly popular choices is called gamification. Here is a quick overview of the process and how some small businesses have implemented it.

What is gamification?

It's human nature to be competitive, and many of us exercise this nature by playing games. Be it team sports, board games, video games, or even office-related games, many of us partake in some form of game on a regular basis. Gamification is the incorporation of game elements, such as points, rules of play, competition, etc. into business-related processes.

By implementing game elements into areas like marketing or training, you can drive engagement, while also collecting better data, primarily because most people will be more willing to provide relevant information when they are invested in a game.

When it comes to implementing these elements into business processes, many companies tend to focus on either customer gamification or employee gamification.

Customer gamification

The vast majority of customer-oriented gamification relates to rewards programs and repeat customers. Small to medium businesses who have successfully implemented these elements usually do so via social media and mobile apps. Repeat customers gain points for each purchase and when they reach a certain level receive a freebie perhaps or a rebate. This in turn drives the need to keep purchasing and to "win".

Many businesses have been successful in implementing this game characteristic into social media, where people who interact gain levels and therefore access to such benefits as discounts. Businesses implementing customer-oriented gamification often see both increased engagement and better data flowing into the organization. In fact, many businesses have found that the data implemented through these elements has been useful in decision-making and overall business intelligence efforts.

Employee gamification

Employee-based gamification is usually employed by businesses to encourage teams and individuals to work together towards a common goal. For example: Implementing a point or badge-based sales system where at certain sales levels badges are awarded, which can then be used for a reward, has proven to be incredibly successful for many sales-oriented companies. Publicly announced results and recognized rewards can also be a great employee motivator.

As with customer gamification, employee gamification can be a great source of data. For example, by tracking where employees are, and their results, you can quickly see weak spots or places where help may be needed. Essentially, more data means the ability to make better decisions.

Should my company implement gamification?

While this may sound like an exciting, and useful tactic to implement in your business, it's not for everyone and it won't fit well with all activities. What you should do is to look at whether the objectives and goals of the program you wish to implement can also be paired with gamification.

If you find that gamification, or elements of it, won't benefit your business program, then it's best not to implement it for the sake of it.

How to implement gamification

There are a wide number of mobile apps developed around gamification, along with social elements and ideas. What we suggest is talking to us to see how we can help first. We can work with you to find solutions and ways to implement your solutions. Contact us today to start the game of business success.
Published with permission from TechAdvisory.org. Source.

September 11th, 2014

BI_Sep08_CPredictive analytics has long been employed by large-scale businesses to help make decisions and long-term business predictions. Now, small to medium businesses are starting to integrate these methods in larger numbers. A common stumbling block for many managers and owners however is that this can be a highly overwhelming concept. To help, here is an overview of the three main components of predictive analytics business owners and managers should be aware of.

Together, these three elements of predictive analytics enables data scientists and even managers to conduct and analyze forecasts and predictions.

Component 1: data

As with most business processes, data is one of the most important and vital components. Without data you won't be able to make predictions and the decisions necessary to reach desired outcomes. In other words, data is the foundation of predictive analytics.

If you want predictive analytics to be successful, you need not only the right kind of data but information that is useful in helping answer the main question you are trying to predict or forecast. You need to to collect as much relevant data as possible in relation to what you are trying to predict. This means tracking past data, customers, demographics, and more.

Merely tracking data isn't going to guarantee more accurate predictions however. You will also need a way to store and quickly access this data. Most businesses use a data warehouse which allows for easier tracking, combining, and analyzing of data.

As a business manager you likely don't have the time to look after data and implement a full-on warehousing and storage solution. What you will most likely need to do is work with a provider, like us, who can help establish an effective warehouse solution, and an analytics expert who can help ensure that you are tracking the right, and most useful, data.

Component 2: statistics

Love it, or hate it, statistics, and more specifically regression analysis, is an integral part of predictive analytics. Most predictive analytics starts with usually a manager or data scientist wondering if different sets of data are correlated. For example, is the age, income, and sex of a customer (independent variables) related to when they purchase product X (dependent variable)?

Using data that has been collected from various customer touch points - say a customer loyalty card, past purchases made by the customer, data found on social media, and visits to a website - you can run a regression analysis to see if there is in fact a correlation between independent and dependent variables, and just how related individual independent variables are.

From here, usually after some trial and error, you hopefully can come up with a regression equation and assign what's called regression coefficients - how much each variable affects the outcome - to each of the independent variables.

This equation can then be applied to predict outcomes. To carry on the example above, you can figure out exactly how influential each independent variable is to the sale of product X. If you find that income and age of different customers heavily influences sales, you can usually also predict when customers of a certain age and income level will buy (by comparing the analysis with past sales data). From here, you can schedule promotions, stock extra products, or even begin marketing to other non-customers who fall into the same categories.

Component 3: assumptions

Because predictive analytics focuses on the future, which is impossible to predict with 100% accuracy, you need to rely on assumptions for this type of analytics to actually work. While there are likely many assumptions you will need to acknowledge, the biggest is: the future will be the same as the past.

As a business owner or manager you are going to need to be aware of the assumptions made for each model or question you are trying to predict the answer to. This also means that you will need to be revisiting these on a regular basis to ensure they are still true or valid. If something changes, say buying habits, then the predictions in place will be invalid and potentially useless.

Remember the 2008-09 sub-prime mortgage crisis? Well, one of the main reasons this was so huge was because brokers and analysts assumed that people would always be able to pay their mortgages, and built their prediction models off of this assumption. We all know what happened there. While this is a large scale example, it is a powerful lesson to learn: Not checking that the assumptions you have based your predictions on could lead to massive trouble for your company.

By understanding the basic ideas behind these three components, you will be better able to communicate and leverage the results provided by this form of analytics.

If you are looking to implement a solution that can support your analytics, or to learn more about predictive analytics, contact us today to see how we can help.

Published with permission from TechAdvisory.org. Source.

August 14th, 2014

The data your business generates and captures is among the one of the most important assets available to yourself and your and employees. Unfortunately, the amount of data available is growing exponentially and it can quickly overwhelm many positions. One solution that allows businesses to better manage data is the data warehouse. The only question, is how can you tell when you need one for your business?

What is a data warehouse?

A data warehouse is a system used by companies for data analysis and reporting. The main purpose of the data warehouse is to integrate, or bring together, data from a number of different sources into one centralized location. The vast majority of the data they store is current or historical data that is used to create reports or reveal trends.

Possibly the biggest benefit of a data warehouse is that it can pull data from different sources e.g., marketing, sales, finance, etc. and use this different data to formulate detailed reports on demand. Essentially, a data warehouse cuts down the time required to find and analyze important data.

While not every business will need one right this minute, a solid data warehouse could help make operations easier and more efficient, especially when compared with other data storage solutions. That being said, it can be tough to figure out if you actually need one. In order to help, we have come up with five signs that show your business is ready to implement a data warehouse.

1. Heavy reliance on spreadsheets

Regardless of business size, the spreadsheet is among the most important business tools out there. Used by pretty much every department in a company, they can be a great way of tracking data. The problem many business owners run across however is that spreadsheets can grow to immense sizes and can become unwieldy.

Combine this with the fact that each department has spreadsheets that you will likely need to pull data from in order to generate a report. If this is the case, you are creating manual reports, which can take a lot of your time.

If you are struggling to find the data you need because it is spread out across different sheets, in different departments, then it may be time to implement a data warehouse.

2. Data is overwhelming your spreadsheets

Spreadsheets are designed to operate with a set amount of data (rows and columns). Reach, or exceed this limit, and you will find that the file becomes sluggish or will downright prevent you adding more data.

While it can take a while to get to this point, companies will reach it if they keep adding to their data. At this point you will see a drop in productivity and overall effectiveness in how you use your data. Therefore, a data warehouse that can combine data from different sheets may be a great solution.

3. You spend too much time waiting

If you set out to develop a report, only to find out that you need to wait for colleagues to provide the information on their spreadsheets, or to analyze their data, you could find yourself waiting for a longer than expected time.

This makes you highly ineffective and can be downright frustrating, especially if employees are too busy or just can't provide the information needed. Implementing a data warehouse can help centralize data and make it available to all team members more effectively. This cuts down the time spent actually having to track it down and communicating with colleagues.

4. Discrepancies in data and reports

Have you noticed that when team leaders or members in different departments create reports that the data or findings are different from yours, or other reports? Not only is this frustrating, it is also time consuming to sort out and could lead to costly mistakes.

This can be amplified if some departments have data sources that they don't share with other teams, as this can throw doubt into the solidity of your data and other reports. If you have reached this point, and realize that there are discrepancies in your data, it may be time to look into a data warehouse which can help sort out problems while ensuring mistakes like duplicate data are eliminated.

5. Too much time spent generating reports

Ideally, we should be able to generate a report using existing data almost instantly, or with as few clicks as possible. If you find that when generating a report you have to keep going to different sources to check if the data is updated, or to keep manually updating other sources, you could quickly see the amount of time needed to develop a report grow.

Because data warehouses consolidate data, you only have to turn to one source for data. Combine with the fact that many data warehouses can be set up to automatically update if source data is updated or changed, and you can guarantee that the data you are using is always correct.

Looking to learn more about data warehouses, or about the different data solutions we offer? Contact us today.

Published with permission from TechAdvisory.org. Source.

July 10th, 2014

BI_July07_CTake a step back and think about the data available to your business. Chances are it has grown exponentially, and will likely continue to do so into the future. While this can be useful as more data equals a better, clearer picture of what is going on in your business, there is still a large amount of data that is useless. In order to prevent you and your company from being overwhelmed, you should have a well defined data collection system in place.

What is well defined data collection?

Everyone collects data, even people who don't use computers. The key to being able to successfully leverage the data you have available to your business lies in a strong foundation - in this case, how you collect your data. With an appropriate system in pace you will know what data to collect and measure, and just how important it is. From here, you can more effectively analyze and interpret it, allowing you to make more informed decisions.

If you are looking to implement a new data collection system, or improve on how you currently collect it, here are six tips that can help:

1. Think about what customer interactions are important

Often the most important data you need is in relation to your customers. Your first step should be to define important customer interactions. For example, if you own an online store, you will likely want to know where your customers come from, the items they click on, items they add to their cart, and items they ultimately buy.

By first identifying important interactions to track, you can then look for metrics and data collection methods related to these interactions. This makes it easier for you to track the most important data.

2. Think about what behavior-related data is important

Don't just focus on those customers who have completed a purchase or followed through the whole business chain. Think about what behavior could produce data that is important to your organization.

To continue the online store example from above, this information could include how far down the page people scroll, how many pages deep they go when looking at product categories, how long they spend on a site, and where those who don't convert leave from.

Collecting and analyzing data like this can be a great determinant of what is working well and what needs to be improved upon.

3. Look at important metrics you use

Sometimes the way you collect your data will depend on how you plan to measure it. This includes the different metrics you use to define the success or failure of marketing plans, sales initiatives, and even how you track visitors.

Be sure to identify which ones your business currently uses, as these will often point you towards the relevant data you will need to collect.

4. Identify the data sources you are going to use

In many businesses there are redundancies with data collected. For example, a CMS (content management system) will often have some of the same data points as Web analytics, or a POS (Point of Sale) will have some of the same data points as an inventory system. Due to this, you are going to have to identify what systems will provide what data.

On the other hand, many businesses use data from multiple systems for one key metric. In order to ensure that you are collecting the right data, you will need to identify these sources and ensure that they are compatible with your data collecting system. If they aren't, you could face potential problems and even make wrong decisions based off of incomplete data, which could cost your business.

5. Keep in mind who will be viewing the reports

When implementing data collection systems and subsequent data analysis systems, you will likely start generating reports related to this data. It is therefore a good idea to identify who will be reading these reports and what the most important information they will need is.

This information will be different for each audience, so be sure to identify what data they judge to be important. For optimal results, you should think about who will be reading the data reports and what relevant data needs to be collected in order to generate them.

6. Set a reasonable frequency for collection and analysis

This can be a tough one to get right, especially if you work in an industry with high fluctuation or your business is in a constant state of change. Your best bet is to look at when you think you will be needing data. For example, if you are responsible to submit a monthly sales report it might be a good idea to collect data on at least a bi-weekly basis in order to have enough to develop a report at the end of the month.

You should also look at who will be getting the reports and how long different campaigns or business deals will be in place. The frequency will vary for each business, so pick one that works best for your systems and business.

If you are looking to implement a data collection system, contact us today to see how we can help.

Published with permission from TechAdvisory.org. Source.

June 17th, 2014

businessintelligence_June16_CMost businesses are continuously looking for ways to improve visibility, efficiency, and gain valuable insights into consumer behavior. By utilizing your company’s business intelligence (BI) system, you can achieve all this and more. Many companies spend anywhere between USD $100 thousand and $1 million for their BI system but fail to make proper use of it. Do you think your BI system could use an extra push in the right direction?

5 ways to improve business intelligence value

1. Pump customer data into your analysis Most companies are chasing after a 360 degree view of their customers, and while this seems like an elusive goal, it can be achieved. Take the first steps by integrating data from your CRM, accounting, and customer support systems into your BI dashboards and reports to allow analysis of customer growth, profitability, and lifetime value. Understanding these KPIs can help you spot trends as well as identify opportunities to cross-sell or upsell. 2. Set up alerts and delivery Your business intelligence can instantly improve its standing and value with alerts and report delivery. Notifications, in the form of email alerts, are useful for managers to keep an eye on business operations without having to log into the BI system. The added perk here is that managers can stay on top of KPIs and new updates even when they're on the move as reports and dashboards can be emailed to them according to a set schedule. 3. Reassess your dashboards If it’s been a while since your BI dashboards were first designed, try updating them with modern charts and stylish fonts. While this may seem unnecessary to some companies, attractive dashboards attract more users and you’ll likely see an uptick in adoption after a dashboard refresh. 4. Deploy existing content on mobile devices By increasing your BI content’s availability, you can quickly increase the number of users accessing it. A great way to do this is by deploying your dashboards and reports on mobile devices. This is especially useful for decision makers who travel frequently or need to be able to access KPIs from anywhere; after all it’s easier for them to pull out a phone or tablet rather than drag out a laptop. Your BI system likely includes some way to make your existing BI content mobile. Allowing users to access BI the way they want can be a great way to boost your BI value. 5. Make it predictive While BI has traditionally been used to present historic data for manual analysis, now more than ever it’s incorporating predictive analytics. By leveraging stored data from your BI system and applying predictive analytics, you can project future performance and make better business decisions based on more accurate forecasts.

Modern BI platforms come with many options, from multi-data source connectivity to mobile BI. It is up to you to leverage the full breadth of your BI software’s capabilities to ensure that you’re getting all the value it can deliver. Looking to learn more about business intelligence and its functions? Get in touch.

Published with permission from TechAdvisory.org. Source.

May 22nd, 2014

BI_May19_CPredicting the future is a skill many business owners wish they could have. After all, if you could tell what will happen six months from now, you might find that business would be infinitely easier. Of course, this is not possible to such a precise extent. That being said, businesses do have tools at their disposal that they can use to attempt to predict the future, such as predictive analytics.

What is predictive analytics?

Before looking at why businesses might want to implement this type of analytics into their operations, it's worthwhile defining what exactly predictive analytics is. Simply put, predictive analytics is a form of business intelligence that focuses on combing existing information for patterns and useful data that can then be used to make predictions on future outcomes or to identify trends.

It is important to stress that this form of analytics does not tell you what is going to happen. Instead, it is used to figure out what might happen. Think of it as similar to a weather forecast for your business - meteorologists can never tell you what the weather will be like over the next week, they merely use the data they have at their disposal to forecast what the outlook is likely to be in the next few days.

The vast majority of companies who apply these analytics to their business often do so to gain a better understanding of their customers, partners, and other stakeholders. From this they can better identify possible risks and opportunities.

Five reasons to use predictive analytics:

  1. Compete better - Companies who use predictive analysis can generally compete smarter. This is because they can leverage existing data to figure out why their customers choose them. By doing the same, you can then focus on highlighting your strengths. This is especially useful if you have some quality strengths to play with.
  2. Work out how to better meet demand - If utilized effectively, you can predict with some accuracy the level of demand for your products, including sales of specific items at certain times, and high/low times for customer visits. From here, you can schedule deliveries or staff to ensure products and staff will be available.
  3. Exceed expectations - While forecasting customer demand is important, what really keeps customers returning is when you exceed their expectations. One of the best ways to do this is by offering products or services the customers need them; or even before they need them or know they do. By understanding customer buying habits you can develop individualized campaigns that focus on their upcoming needs; offering useful products and/or services.
  4. Increase efficiency - Analyzing your existing data can help predict when you may have supply issues, or where production problems may crop up when launching a new product or service. With this warning system in place you can take steps to limit any negative repercussions or make provisions to guard against a predicted problem. This then can help increase overall efficiency.
  5. Better able to reach clients - By first tracking customer touchpoint data - when did they contact you and how - you can then use this data to forecast when your customers will be looking at social media, more willing to read an email you send, and even when they might be more willing to talk with you on the phone.
These are just a few of the reasons businesses use predictive analytics in their companies. If you are curious to learn more about how to create success for your business and the technology systems that support and allow you to utilize predictive analytics, contact us today for a chat.
Published with permission from TechAdvisory.org. Source.

April 25th, 2014

BI_Apr22_CWith the increasing amount of technology and data available to a business, the need to make informed decisions and utilize the data at hand is increasingly important. One function that helps with this is Business Intelligence (BI). While BI is becoming ever more popular with small business owners, there are common misconceptions about BI that could hinder its effectiveness.

But many of these misconceptions are easily clarified and addressed. See how by taking a look at these tips.

1. Business Intelligence is all about reports and dashboards

One of the things that make business intelligence sound intimidating is the notion that businesses will be bombarded with daily reports and have to make use of complicated dashboards in order help to understand how it works and get it operational. While there are standard tools that small businesses will need to use to gather information that will help with their operations, these tools should not be seen as an inconvenience. The systems and processes in BI can be simplified in a way that it doesn’t limit resource gathering but actually enhances efficiency and profitability.

An executive, for instance, can easily look into the sales numbers of a given month, without having to go over other variables and metrics. Other models of BI can cater to more than just reporting stats and data, as analysis can be collaborative and interactive, thus providing more efficient solutions that will deliver the correct information to the people who rely on the data for their decision-making.

2. The tools are the same for all organizations

The assumption is that whether a company is big or small, the tools in business intelligence work the same. This is what makes small businesses hesitant to apply such concepts, thinking that it will not have any practical use in an organization of their size.

But the truth is, every BI strategy is unique, and as a company, you can tailor these strategies to fit in with the way you operate. Before adopting any solution, however, you will first have to evaluate what specific needs BI must address using the data architecture, so that it will measure your requirements correctly.

3. It can only measure big data

Large corporations can maximize the tools to use in BI because they have larger needs to fill, and they also have all the resources. But what about small companies that may not necessarily need big data?

The thing is, any company that has data will have a use for business intelligence. Small businesses can start with simple and basic solutions, for instance Google Analytics, and then later on, expand to a more comprehensive tool as the organization grows. Business intelligence measures the quality of data, and not the quantity, so you can accomplish something even with very few resources.

4. It takes up IT resources

While BI used to be considered the responsibility of an IT team or expert, now small businesses, which may not have had the resources to in the past to outsource such resources, can use the tools for themselves. There are solutions out there that offer low maintenance, self-service systems wherein reports and dashboards are created and analyzed without the need for an IT expert whatsoever. There are some advantages to having professional IT help sometimes though, but for small businesses, a user-friendly BI system may be sufficient to cover most of your needs.

If you'd like to know about how you might be able to develop your business intelligence systems further. Consult a reputable IT services provider now.

Published with permission from TechAdvisory.org. Source.

March 27th, 2014

BI_March24_CBusiness Intelligence or BI applications are used by businesses from different fields for their information analysis. These tools help determine what individual businesses may be doing right or wrong, which can help them decide on the best path to take to reach their goals. BI tools are helpful to many businesses. However, some business owners question how these tools can be used in different departments.

There are various BI tools available nowadays that support small to large companies. You can find Business Intelligence tools that fit your company’s size, needs and budget. These applications can be used in different areas of the business:

Marketing Department

A marketing department is responsible for promoting a company’s products, services and brand to increase public awareness. With successful marketing, a business can attract potential clients that can be possibly turned into creating sales revenue. The company can use BI to determine which campaigns are successful or not, as the case may be. Through this, investments can be focused on those campaigns that work whilst avoiding those that have previously failed.

Sales Department

Sales managers and supervisors can also use BI to analyze successful deals, as well as those that they have lost, to see what strategies have worked. The system can also help determine which sales teams hit or exceed set goals in order to analyze what they are doing right. Moreover, this helps determine which products or services are most saleable so these can be pushed further to attain more goals.

Finance Department

BI software makes analyzing, reporting, and managing financial data more convenient. Those who are involved in the process can easily access the information they need through the system. Analysis is easier as the data is organized and accurate. Money in and money out can also be tracked with greater efficiency.

Moreover, these tools often come with features that allow users to create scenarios and determine the possible results from there. This is extremely helpful in deciding on the best action to take as the tool gives you a view of the probable outcome. The success rate is higher if forecasting using a BI tool.

Inventory

Business Intelligence also plays a vital role in inventory tracking of products, items or supplies. For instance, companies in the retail industry can track the movement of products or items from the suppliers to the warehouse and on to their delivery to clients. Any problems encountered in the process can be quickly identified so they can be fixed in time.

Items in demand can also be pinpointed, as well as low stock and overstocks. Items that are low in stock can be ordered immediately, especially if they are in demand, to ensure that the needs of clients are met. This also lets you avoid overstocking, which can be a waste of money when investment is better used for fast moving items.

These are just some of the ways businesses can use BI in their operations. If you have further questions about the topic, do not hesitate to give us a call. We’ll be more than happy to assist you.

Published with permission from TechAdvisory.org. Source.

February 28th, 2014

BI_Feb24_CIn order for a business to get more out of their existing and future data, many are relying on Business Intelligence (BI) solutions. If you are looking into a BI system for your business you will likely come across data related terms that are important to know about. Three of the most commonly asked about are data mining, data warehouse, and data mart.

What is a data warehouse?

The concept of a data warehouse is an interesting one and also a difficult one to define and pin down largely because it can cover such a broad area. The most concise definition we can give is that it is a database that integrates data from many different locations and databases into one consolidated database.

Data warehouses store both current and historical data, and rarely contain unique data. Instead, they aggregate data from other sources in order to make this more accessible. They might store important information from sales, marketing, ERP, customer interactions, and any form of database in order to quickly generate BI related reports.

The name undoubtedly conjures up the idea of a large warehouse-like building storing infinite amounts of data. However, most data warehouses are actually tables which are created by taking data from various sources and cleaning it up so that relevant data is stored in the warehouse in a way that makes it easier to reach when needed.

What is a data mart?

A data mart is a smaller data warehouse that stores data. These are based on a specific area or business function e.g., finance or marketing, etc. In fact, most modern data warehouses are actually made up of a series of smaller data marts.

The key difference between a data mart and a data warehouse is that data marts are usually smaller, focusing on one specific area, while a data warehouse covers the whole organization.

What is data mining?

When talking about Business Intelligence, many experts will refer to data mining. This is the act of analyzing data in order to identify patterns. The data that is mined can then be transformed into useable information. Many companies store this mined data in databases, a data warehouse, or a data mart.

Want to learn more about these terms and how your company can benefit from a BI solution? Contact us today.

Published with permission from TechAdvisory.org. Source.