Regardless of what job you do, you will regularly make decisions, and in order to make these decisions, you need data. Data surrounds us and, whether we know it or not, data informs our decisions so being able to analyse data means being able to make better decisions.
As technology has progressed, it's become easier to put systems in place to handle data, but it's also increased the amount of data we have available to us so we're constantly looking for easier ways to see the story behind the data. The introduction of computerised spreadsheets was a big moment in data analysis because suddenly anyone could input information, run a formula or create a graph and find an answer and as these tools have progressed, the access to information has exploded!
As a trainer and consultant, I've chosen to focus on data analysis and using Excel and Power BI in order to analyse data. I spend a lot of time teaching people how to use these tools but I also spend time teaching different forms of data analysis because what a lot of people don't realise is that knowing WHAT questions to ask is as important, if not more important, than being able to put together an impressive dashboard.
The two most basic forms of data analysis are comparison analysis and trend analysis so I'm going to start with them.
A comparisons shows you things like what items sells the most or which product is not doing well. You can compare different months or regions to see where the biggest profit or lowest turnover is, you can also see how much of a difference there is between different products or time periods. Tables are the simplest way to do a comparison analysis, or if you'd like to visualise it then a column or bar graph is best. A pie chart is useful if you're only comparing two or three items but should be avoided for more than this.
The column or bar provide a good indication of how large or small an item is in proportion to the other items that are being displayed. If there are a large number of items in the graph then a bar graph is a better option as the labels are displayed horizontally and are easy to read.
Trend analysis is about understanding how data changes over time. Trends can be understood by many different time dimensions such as year, quarter, month, day of the month, day of the week, week number, hour and second.
Understanding your data over different time periods will provide important insight into whether things are moving up, down, staying stable or is volatile. The analysis of different time periods allows you ask a variety of important questions such as:
When working with trends you should use a line graph or area graph. The line graph will provide a good understanding of the general trend over time. A trend line is often added to the line graph and provides a good visual indicator of the trend.
An area graph is a good option for when you want to include a comparison and see how it is trending. For example an item could start off small, but grow over time, an area graph is a good method to display this information.
In the fourth video of our Power BI series we will show you how to easily edit data in Power BI.
Discover how Power BI makes data analysis much simpler!
There is no shortage of data available today, with huge amounts of it being constantly generated by ERP systems, POS systems, PLC systems and Biometric systems, to name but a few.
Companies are needing to find ways to effectively analyse the data, to leverage the information and make informed decisions, however many of the options available are very expensive and require a lengthy period of implementation, plus human natures makes most staff resistant to learning new systems so all in all, decision makers are faced with a challenge on many levels.
Excel has become the main tool for manipulating and analyzing data, and developing reports in business today, and with the release of Excel 2013, Microsoft introduced even more powerful business intelligence tools.
To understand how the Excel Business Intelligence tools work together in Excel 2013, you need to understand the function of each tool and how it fits into the “traditional” Excel model.
Extract, Transform, Load
Power Query is an intuitive, easy to use tool for importing, transforming and working with data.
Spreadsheets are the basic feature of Excel. Launched in 1985, there are now over 700 million Excel users worldwide.
Tables were introduced in Excel 2007, they allow you to do quick sorting and analysis of your data.
Power Pivot was introduced as a free add-in for Excel 2010, it gives you the ability to access, process and handle well over a million rows of data, more than a standard spreadsheet can process.
Pivot Tables are an easy and convenient way to build intelligent, flexible summary tables, allowing you to quickly derive insight from your business data.
Pivot Charts enable you to visualize a PivotTable. You can quickly change a portion of data displayed, making PivotChart ideal for presentation of data in reports.
Power View is an interactive data exploration, visualisation and presentation tool that gives you strong insight in to your business .
So what is the bottom line?
Before spending time and money on new data analysis tools, find out exactly how learning how to use all the Excel Business Intelligence Tools effectively will allow you to quickly and easily analyse your data and gain insight into it without any major resource investments.
Over a billion people around the world use Excel today, far more users than any other BI vendor can claim.
Original article from Thor Olavsrud (IT Security, Open Source, Microsoft Tools and Servers for CIO.com). Follow Thor on Twitter @ThorOlavsrud.
Companies that rate themselves substantially ahead of their peers in their use of data are three times more likely to rate themselves as substantially ahead in financial performance, according to findings from the Economist Intelligence Unit.
Working with and making business decisions based on data is good for your company's bottom line. Companies that have embraced a data-driven culture—rating themselves substantially ahead of their peers in their use of data—are three times more likely to rate themselves as substantially ahead of their peers in financial performance, according to findings by the Economist Intelligence Unit in a survey sponsored by Tableau Software.
In October 2012, the Economist Intelligence Unit surveyed 530 senior executives from North America, Asia Pacific, Western Europe and Latin America across a broad range of industries. The survey found that the most successful companies have adopted a data-driven culture in which they maximize the use of data by providing necessary training and promoting the sharing of data across all levels of employees and departments.
"The importance of data-driven thinking is not new," says Jim Giles, author of the Economist Intelligence Unit report, Fostering a Data-Driven Culture. Many executives are familiar with the concept. The rise of data-driven companies, from Facebook to Walmart, shows how powerful the approach can be. But what does it mean in practice? And what are the benefits of adopting a data-driven culture within an organization?
Data-Driven Culture Is About More than Data Specialists
"Let us start with what a data-driven culture is not," Giles says "It is not a belief that data are an issue for someone else in the company, a job for a data specialist or perhaps the IT department. There is still a perception that a data specialist, perhaps a recent statistics graduate, should be parachuted into an organization to advise on how to work magic with data, much as a computer security expert would be called on to help shore up a company's IT networks."
This, Giles says, is flawed thinking. Instead, he says, forward-looking organizations don't concentrate data in the hands of an individual or small group but integrate data into their day-to-day operations.
"They are placing data at the heart of almost all important decisions," he says. "And they are tolerant of questioning—even dissent—about business decisions being made, as long as the questioning is based on data and their analysis. This is what it means to adopt a data-driven culture."
Top-Performing Companies Have Adopted a Data-Driven Culture
And the adoption of data-driven culture is bearing fruit for many organizations. The Economist Intelligence Unit found that only 11 percent of respondents felt their organizations make substantially better use of data than their peers. But more than one-third of that group was comprised of top-performing companies. On the flip side, of the 17 percent of executives that said their companies lagged peers in financial performance, none felt their organizations made better use of data than their peers.
In all, 76 percent of executives from top-performing companies cited data collection as very important/essential, compared with only 42 percent from companies that lag their peers in performance.
The differences are stark. But adopting a data-driven culture is not necessarily easy, especially for older companies that have achieved success with minimal use of data.
"Many of my clients are clearly aware of the importance of data," says Jerry O'Dwyer, a principal at Deloitte Consulting. "But they don't know where to start in termsof where they should focus to get the most value, as well as how to translate the data into actionable insight."
In some industries, executives may perceive a shift to a more data-driven approach as a threat. For instance, marketing has been the domain of creative for decades, but now, Giles says, it is as much a quantitative science as an exercise in art and design. Executives who built their careers on smart, instinctive decisions may perceive their value as declining as data's star rises.
Data-Driven Culture Requires a C-suite Champion
One of the most important steps, Giles says, is to break down data silos and promote sharing. More than half of respondents from top-performing companies said that promotion of data-sharing helped generate a data-driven culture in their organization. Such sharing does not arise organically. Someone in the C-suite needs to champion data-driven decision making and use top-down mandates and guidance to drive the shift in culture.
"Someone needs to see the appeal and step up," says Sidney Minassian, CEO of Contexti, a big-data analytics company that operates in the U.S., Australia and Asia. "It could be anyone from the C-suite."
More than two-thirds of executives from top-performing companies in the survey agreed, citing the importance of C-level leadership on data issues.
Of course, even with C-suite buy-in, there are challenges to integrating data use into the heart of an organization, not the least of which is training employees to leverage data and recruiting and retaining data specialists for tasks like predictive modeling. Nearly 70 percent of respondents said recruiting and retaining people who are effective at analyzing data is "somewhat" or "very" difficult. Underperforming companies, as well as companies in the Asia-Pacific region, rated the problem even more severe. Respondents cited lack of professional expertise among applicants, a shortage of analysts in their sector and high salary costs as the principal reasons for the difficulty.
Data-Driven Companies Democratize Data
However, the top-performing companies don't just leave data in the hands of specialists. They seek to democratize data use. Fifty percent of the top-performing companies said training employees to be more data literate is highly important.
Colin Hill, CEO at GNS Healthcare notes that in-house experts create the algorithms behind the tools it uses to assess the comparative effectiveness of different drugs, but the tools themselves are designed to be used by employees across the healthcare industry.
"Part of this is about making the complex simple," Hill says. "Computers are very good at the complex, but ultimately we have to break it down to the human level."
"Leading companies realize that being successful means giving people the opportunity to work with data," says Elissa Fink, chief marketing officer at Tableau Software. "Making data available and easy to use for all employees can transform an organization's culture. It's good for the company's bottom line."
Common Features of Data-Driven Companies
Ultimately, while there is no one path to becoming a data-driven company, those organizations that have achieved success do share some common features:
This is the first video in a five-part series showing you how powerful the Excel "Power" tools are and how learning to use them can vastly improve your productivity, while at the same time giving you insight into your business far beyond anything you ever imagined.
The Complete Introduction to Business Data Analysis teaches you how to apply different methods of data analysis to turn your data into new insight and intelligence.
The ability to ask questions of your data is a powerful competitive advantage, resulting in new income streams, better decision making and improved productivity. A recent McKinsey Consulting report has identified that data analysis is one of the most important skills required in the American economy at the current time.
This course focuses on the following different methods of analysis. During the course you will understand why the form of analysis is important and also provide examples of using the analysis using Excel 2013.
The following methods of analysis are included:
The Complete Introduction to Business Data Analysis is designed for all business professionals who want to take their ability to turn data into information to the next level.
If you are an Excel user then you will want to learn the easy to use techniques that are taught in this course. This course is presented using Excel 2013. Excel 2010 can be used for the majority of the training exercises. Small parts of the course do use Excel Power Pivot and Power View.
Please note that this course does not include any complicated formulas, VBA or macros. The course utilizes drag and drop techniques to create the majority of the different data analysis techniques.
For more information, visit our Online Course page
The business world has changed fundamentally over the past few years.The speed of change means that mangers need to be able to make decisions quicker, but at the same time need to be able to process far more data to be able to make an informed decision.
Business today has no lack of data – transactional systems record every part of the business process. The key challenge for managers is how to turn the vast quantities of data into useful insight and information.
In recent years new technologies has allowed vast quantities of data to be processed, analysed and increasingly visualised. The new technologies allow management to understand trends, identify outliers and to see new patterns in the data that could not be processed previously.
Excel as a Business Intelligence Tool
Excel has become the main tool for manipulating and analysing data, and developing reports in business today. With the release of Excel 2013, Microsoft introduced even more powerful business intelligence tools.
To understand how the Excel Business Intelligence tools work together in Excel 2013, you need to understand the function of each tool and how it fits into the “traditional” Excel model:
How is this relevant to Business Users?
If you work with Excel, using Spreadsheets and Tables to build reports, you probably find yourself regularly copying and pasting data between worksheets then clicking the same sequence of buttons to clean and shape the data.
You’ve probably found that complex formulas, dirty data and inevitable errors take up a lot of your time, making month end reports a nightmare that consumes large portions of time you don’t have.
Learning how to use all the Excel Business Intelligence Tools effectively will free you from the dull, repetitive tasks and give you time to focus on what’s important, analyzing your data and gaining insight into it.
This will enable you to make good decisions about your business.