Gathering Requirements for Analytics: Mastering the Art

In the fast-paced business landscape, analytics stands as a pillar of informed decision-making. But before diving in, it’s essential to gather the necessary data and inputs effectively. This post will guide you through the prerequisite considerations of the ‘Who’ and ‘Why’. We then focus on primary perspectives of gathering analytics requirements the ‘What’, ‘Where’, and ‘How’.

Prerequisites

Before we delve into the details, make sure to:

• Identify the Stakeholder Audience: Know who will be the primary beneficiaries of the analytics insights.
• Define Analytics Use Cases: Know why analytics will be applied.

For example, will department heads be using this to assign budget and resource to new initiatives? Or is this a board report to identify wider trends in the organisation?

Requirements – What

What is the (key) data to include in the analysis?

• Measures – Numerical Data: Identify the key numerical data that will form the core of your analysis. E.g. Opportunity value
• Dimensions – Contextual Data: Include relevant contextual data to add depth to your numerical data. E.g. Customer Industry
• Dates: Incorporate critical dates to align your analysis with specific time frames. E.g. Close Date

Chris Taylor

Principal Salesforce Consultant

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Requirements – Where

To effectively analyse data, you need to know where to find it. Potential data sources include:

• Salesforce Objects: Leverage the data stored within Salesforce objects.
• Marketing Cloud: Utilise the extensive data available within the Marketing Cloud.
• Spreadsheets: Access and use vital data stored in spreadsheets.
• Cloud Databases: Retrieve data from your cloud databases.
• Unknown: Sometimes, you might need to revisit the process that gathers the data to understand if/where could it be retrieved from.

Requirements – How

Next, determine how to combine data from various sources effectively. Here are a few strategies:

• Salesforce Relationship Field: Use relationship fields in Salesforce to link data efficiently.
• Value Matching: Apply value-matching techniques (e.g., matching on name and website) to combine data sources.
• External ID: Utilise external IDs to create connections between different data entities.
• Unknown: In some cases, the method of data combination might need further exploration and even might need to be introduced.

Analytics Tool Selection

Choosing the right analytics tool is a crucial step. Here are a few considerations:

• Default to Reports and Dashboards: In Salesforce we usually use reports and dashboards as default analytics tools.
• Tool Limitations: Be cognizant of tool limitations and plan how to meet your requirements within those bounds.
• Deployment & Utilisation: Think about how the analytics will be deployed and the manner in which they will be used.
• Attention to Detail: Choosing an appropriate analytics tool may depend on what seems like a relatively small aspect.

In conclusion, gathering analytics requirements is a systematic process of identifying the necessary data, locating the sources, and determining how to combine them effectively. Utilise this guide as your practical tool in venturing into data-driven decision-making.