Having a vast amount of data that comes from your own key performance indicators (KPIs) and business intelligence (BI) sources is a good thing. Whether you are a CFO mathematical whiz, running the IT department and have no fear of numbers, or you're a rookie to utilizing BI analysis tools, it is possible to understand what data analysis really means and how to do it successfully. The bottom line is that you need to know how to use your data to make valid projections and decisions for your company, but sometimes you may not know where to start.
Data analysis is a systematic way to use data that you have to make predictions or draw conclusions that you can rely on. In essence, data analysis is a process of looking for and testing for valid differences or changes which can be attributed to a known condition.
Truly extracting the meaning of data requires use of repeated sampling because each individual measurement is a combination of valid fact and error of measurement. Use of repeated measures and replications allows you to estimate the size of the error factor and to tease out the valid fact.
Alright, now that the technical definition is out of the way, let's put it into terms that relate more to the analysis of business intelligence data so you make better forecasting, budgeting and planning decisions for your company.
Applying data analysis to your business in it's simplest definition means that you have established, as a company or enterprise, the KPIs and metrics to measure that will allow your gathered business intelligence data to provide reliable, comparable results.
With those results, you have the available data to visualize both a pathway to hit short term, targeted goals and how you can use these short term goal accomplishments to meet or exceed the big picture, end goal so the company's overall goals are met.
Based on the above explanation, data analysis is how you approach the data results, your understanding of how the KPI and metrics data applies to where you are now, what your competitors are doing, and what actions to take to stay on track for the company's goals for the time period as well as remain competitive in the market.
However, beyond providing an answer to the question "what is data analysis?", the above doesn't provide you with direction as to how to analyze data for success.
Here are seven tips that will give some structure to your data analysis process and allow you to look beyond the fact that numbers are involved, so you are empowered for better planning and decision making.
1. Understand Your Business AND Your IndustryAnalysis isn't only numbers. In order to be able to recognize the indicators for better planning and making decisions to accomplish the goals, you have to understand your company and it's core business and the industry as a whole.
The data, and the KPIs that have been chosen, aren't just numbers. They are an abstract of the real business and what is happening in the industry. They should allow you to use your knowledge of the business and the data to conceptualize as well as make and understand generalizations with similar businesses and the industry. Then you are able to see what happened, what it means for your goals (and the company's) and make an actionable decisions for moving forward.
2. Define Your Goals. Apply the KPIs to Get You There.If you don't know where you are supposed to be going, how can you make any viable decision as to what actions to take for success. Without defining your goals, you can't determine the right KPIs and metrics to measure, and then you really are just looking at numbers.
The first indication that the goals haven't been defined is the question "what can I get from data besides the final numbers?", which goes back to tip #1 because there is no use of the numbers for abstract thinking and it is like trying to get out of a dark room.
You want to be asking "what data do I need to have to accomplish my goals?"--your goals are defined and you want the information, the data, that will tell you what is happening, to make comparisons and ascertain what it means so you are able to make the best decision to keep aim on your goals.
3. Learn the Basics of Both Marketing and Management
No doubt about it, this will help with tips 1 and 2 above. Even if you took both courses in school, your real world experience provides perspective you can't get from textbooks. Whether it's completely new to you or it's a review, your new perspective will allow you to apply the basics of marketing and management for better data analysis.
4. Complicated or Simple, If It Solves the Problems It's a Good Method
If you have a method that provides the information or the result you need to solve the problem, that leads you to the solution, don't feel it needs to be complicated to be valuable.
If you can use elementary school knowledge to solve the problem, why would you open a college textbook?
5. Complicated or Simple, If It Solves the Problems It's a Good Tool
Instead of just doing the normal software name dropping that shows how many systems an advanced data analyst knows, this tip is to make the point that it isn't how many different software tools you use.
Focus on tools that allow you to be nimble and provide the most value to helping you solve the question or problem. Again, if you are able to make solid decisions using a tool, focus on it's value.
6. Use Graphs, Charts and Reports to Visualize. And Speak Using Them, Too.
Visualization is known to be the preferred way to absorb information. Use it to your advantage.
If you're already a numbers person and like to dig into the details, that's great. But speaking numbers doesn't always get your point across, so use visual tools so everyone can see the final results quickly and clearly.
7. Use Business Intelligence Software Tools That Do the Heavy Lifting for You
IBM Cognos and SAP Crystal Reports are two BI software solutions that do the heavy lifting for you. You can have complete data integration across multiple systems, dashboard design capability, and reports, graphs and charts in multiple file formats so your data analysis is simplified and effective.
Conclusion
The analysis of data does require a process and developing skills as to how you use the data gathered, but doesn't have to be overcomplicated. When business intelligence data is applied to established KPIs and Metrics in a disciplined way, you can be more sure of your decisions than you can by "eyeballing" raw data. Having the right BI software solutions that integrates the data from all sources, provides reporting for all roles in the company and allows for customizable dashboard design will allow anyone to analyze for business success.
Take the first step to better data analysis by getting a better grasp on steps and processes for better data interpretation and analysis with our free IT Guide to Data Analysis and Interpretation.