3 Misconceptions On Why Companies Fail To Embrace a Big Data Strategy
In Big Data Part 1, Leveraging Big Data for Sales and Marketing Profitability, we discussed the power of data and how businesses are using data for corporate growth. In this follow up report, we discuss the misconceptions about big data and how you can implement a big data strategy easily and cost effectively.
If you are like many companies without a Customer Relationship Management System (CRM) or Marketing Automation software you probably have multiple systems, all separate, managing your customers, prospects, vendors and marketing initiatives. If this sounds like your company, you probably have an accounting system and a bunch of Excel files. Your sales and marketing teams use different tools and in many cases even your sales people are using different tools from one another. In most businesses these tools are not linked together. Sales and marketing teams work independently. So, how do you determine your sales pipeline? How do you easily acquire a segmented marketing list? How do you track customer sales back to marketing initiatives? Think of the time and expense you waste on pulling this information together. To better understand data, it's important to look at the definition of what data actually is.
The definition of data per Wikipedia:
Data (pron.: /ˈdeɪtə/ day-tə, /ˈdætə/ da-tə, or /ˈdɑːtə/ dah-tə) are values of qualitative or quantitative variables, belonging to a set of items. Data in computing (or data processing) are represented in a structure, often tabular (represented by rows and columns), a tree (a set of nodes with parent-children relationship) or a graph structure (a set of interconnected nodes). Data are typically the results of measurements and can be visualised using graphs or images. Data as an abstract concept can be viewed as the lowest level of abstraction from which information and then knowledge are derived. Raw data, i.e., unprocessed data, refers to a collection of numbers, characters and is a relative term; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data refers to raw data collected in an uncontrolled in situ environment. Experimental data refers to data generated within the context of a scientific investigation by observation and recording.
"...from which information and then knowledge are derived." That's pretty powerful!
So why then do so many sales and marketing teams fail to harness the power of data? Provided below are the three most common misconceptions about a big data strategy.
1. It’s too much work.
Collecting data is not rocket science and it should not be too much work. In today's digital age, there are plenty of incredible tools readily available to help you collect and mine data for both your sales and marketing initiatives. Automated Marketing Software, such as HubSpot or Act|On can help with your marketing efforts. SalesForce.com or Dynamics CRM can help with sales. Connecting sales and marketing tools together can deliver even more value. There is often a learning curve and a paradigm shift in the way you are working when you adopt a new system. With a strong leadership commitment and a focus on making these systems work, the learning curve can be short and provide near immediate results. By the way, Excel is not a good data collection system for sales and marketing purposes. Excel is too much work.
2. It costs too much.
The real question is what is it costing your company to not have these systems and strategies in place? Data is your most valuable asset. With strong data, sales and marketing is easier and delivers greater results. When it’s easier it is more efficient, and when its more efficient we spend less time and money on getting the information we need to make informed decisions. With good data you can focus your time and effort on more productive activities, yielding greater ROI. With good data you can be more efficient and profitable.
3. It’s not important.
Many companies fail to quantify the true financial benefit of data and data systems. Usually the ways in which data can help a company are not independently measured or tracked. As an example, few companies measure the lost opportunities when a sales person leaves the company and all of their sales and prospect data is in an Excel doc on that sales person’s personal computer. Nor do companies measure the opportunity cost of sending an email marketing campaign on the wrong day of the week. What about the additional time of employee resources spent on repetitive data input that can be automated?
The full magnitude of the benefits offered by big data are infinite and far outweigh the cost of the systems and strategies. I often see one single case of using data to make an informed decision provide more value than the cost of the big data effort. How does big data fit into your sales and marketing strategy? What if you could track your customer sales back to the search term they typed into Google months before you even knew they existed? What if you could do this for all customer sales and then find patterns in the data to market and sell more effectively? This is all possible and other companies are already doing it.