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Plan for Your Data Integration Project
Before you build your data integration solution, think about the use case you want to analyze and the data you need to get you there.
Define What You Want To Analyze and Why
Begin by clarifying the purpose of your analysis. What area of your business do you want to target? What specific improvements do you want to make in this area? What KPIs can help you measure improvements? Understand what you want to learn from your data. That way, you can focus your efforts on what’s most important.
Define the Data Elements Needed for Your Analysis
What objects and fields are the most relevant? Start with the obvious ones, and then consider some that aren’t so obvious. Design the dataset that supports the analysis you want to conduct.
Identify the Best Data Source for Each Data Element
For each element, what is its data source? A Salesforce object? An external data source? Some data integration solutions merge data from multiple sources into a single dataset.
Investigate Details About Individual Data Elements
Drill down into these details so that you understand what data cleansing tasks, if any, are required to prepare your data for analysis. For each data element, determine how it is defined in the data source. What is its datatype? How is it formatted? How frequently do format inconsistencies occur? What is its default value, if any? Are null values allowed and, if so, how frequently do null values occur? How accurate are the values? How frequently do inaccuracies occur? Are values written once, or are they subsequently updated and, if so, how often?
Determine What Data You Need for Your Analysis
To yield credible results from your analysis, do you need all rows of data from the data source, or can you filter on a specific segment (subset)? For example, does your analysis target business activity in all countries or just certain ones? Are you focusing on a particular demographic or a range of values, such as customers with a total sales above 100,000?
Determine the Time Window for Your Data
How far back in time do you want to go for your analysis? Two years? Six months? Last week? Filter out any data that is outside your time range. And try to filter data as early as possible to minimize unnecessary data processing.
Commit to Continuous Improvement
We all learn as we go. As you test your data integration and review the results, you can discover ways to improve your outcomes. Perhaps you reconsider previous assumptions, change data sources, add or drop columns, or filter out extraneous rows to focus on just a particular segment. CRM Analytics tools can help you adapt to changes quickly so that you can make incremental improvements while keeping the solution in place.
