Best Practices
April 08 | Blogs
Understanding the Salesforce Data Lifecycle, Part 1: Creation & Capture
Estimated read time 8 min

The average company manages 162.9TB of data. Here’s the kicker: up to 73% of that data goes unused. As the modern business ecosystem grows increasingly data-fused, finding ways to properly manage this mountain of data is a nightmare. For sales, this nightmare comes with sweat-inducing nighttime terrors. Data is the heartbeat of your sales operation, and every unique data point represents a tangible behavior, preference, or concern of your precious clients. So, how do you capture the right data at the right time, utilize and analyze that data appropriately, and design systems that keep your must-have data secure and contained in the midst of a cybersecurity crisis?

Let’s talk about it! In this 4 part series over Data Lifecycle Management (DLM), we’ll cover how organizations can create, capture, maintain, store, analyze, report, archive, and secure sales data at scale. In part 1, we’re talking about creation and capture: the top of your ever-expanding data funnel. Every data-driven decision starts by understanding what data you need to capture in the first place. Plus, we’ll cover how to leverage mission-critical data to create impactful and sales-driven dashboards.

Time to talk data.

What is Data Lifecycle Management?

Every tiny bit of data has a life. It’s born, it’s used or not used, it’s stored, it’s secured, and it’s eventually archived or deleted. How your company handles this data lifecycle — via policies, management, strategy, and technology — is called Data Lifecycle Management (DLM).

At its core, DLM ensures that you’re using the best possible data for analytics, storing data securely against threats, adhering to regulatory guidelines (e.g., SOX, GDPR, CCPA, LGPD, HIPAA, etc.), and deleting or archiving data as necessary.

DLM is broken down into four groups:

  1. Creation and capture
  2. Maintenance and storage
  3. Analyzing and reporting
  4. Archival and deletion

In this particular series, we’re covering DLM in the context of sales teams. However, DLM can be applied to data across your organization, regardless of its origin and use case.

Where Does Data Come From?

Almost all modern sales interactions are processed through CRMs like Salesforce. So, chances are, your business already has a healthy data stream coming from your CRM. Luckily, Salesforce data is generated through a bunch of backend APIs, microservices, and really smart algorithms. So, Salesforce data is often rich in value and relatively hands-off.

That’s great! But auto-generated Salesforce data likely represents only a fraction of the overall data generated by your revenue operations. Every sales and marketing interaction — regardless of where it happens — generates data. Every note written on a salesperson’s computer and every little conversation that happens outside of Salesforce produces valuable signals you could be using to secure leads and conversions.

In general, we can organize data into three buckets:

  • Data capture: This type of data is generated automatically via tools like Salesforce. So, all of those auto-generated fields and savvy Salesforce algorithms use captured data that’s garnered over time. Yes! You can still use this data for outside systems.
  • Data acquisition: This type of data is gained from a third party. You don’t actually enter or capture any of this data, but you still use it to fuel algorithms.
  • Data entry: This type of data is entered manually by employees. Technically, this also includes some Salesforce data. Any data that’s gained when employees type on the keyboard is data entry. The data auto-generated on the backend is data capture.

Note: Companies with robust IoT ecosystems may also have “signal receipts” — which is a type of data recorded by sensors and other devices.

The goal of data creation and capture is to take all of this data, funnel it into the right places, secure it using best-in-class policies and governance models, and use it to create new sales. Often, this data is strewn about your business in a variety of different formats and places (e.g., PDFs, SQL databases, data lakes, S3 buckets, vendor systems, CRMs, point solutions, emails, etc.). So, not only do you need to find where all this data is located (which is challenging enough), but you need to take that data and leverage the right dashboards and analytics to make it useful at scale.

But, before you can begin, you need to understand which data your sales team actually needs.

What Data Do You Need?

Typically, the type of data you need relates to the overall data strategy your business wants to leverage. Most sales teams get this wrong. In fact, McKinsey suggests that 57% of sales teams consider themselves poor at using advanced data analytics — despite their tech-heavy investments. You have to resist the urge to use everything. There is absolutely a thing as “too much data.” It bogs down your dashboards, confuses salespeople, and creates messy, annoying databases.

Instead of thinking about data capture in terms of an overarching catch-all net, think about data capture in terms of goals. What type of data do you need to utilize to successfully complete your current goals. Over time, these goals will change, and you’ll likely use more and more data as you grow out your analytics capabilities. But when you start, try to focus on the golden eggs, not the entire farm.

As an example, here are some of the reasons sales teams often use sales data:

  • Lead generation
  • Lead conversion
  • Lead scoring
  • Customer retention
  • Cross-selling
  • Upselling
  • Targeting

Each of those buckets has unique data points associated with it. For example, lead gen metrics often involve age, income, firmographics, decision-making status, etc. Lead scoring metrics, on the other hand, might include behaviors taken (e.g., webinar views, whitepaper downloads, etc.), lead source, and engagement. So, how do you know which metrics belong to which bucket? You start by discovering which questions you’re trying to answer. For customer retention, those questions may be:

Why are people leaving?

    • Example metrics: Net Promoter Score (NPS), redemption rates, etc.
      What types of people are leaving?
    • Example metrics: firmographics, demographics, etc.
      How many people are leaving?
    • Example metrics: churn rate, customer retention rate
      What is retention costing you?
    • Example metrics: customer lifetime value, days sales outstanding, etc.

Once you discover which metrics you need to measure, it’s time to start capturing that data and storing it appropriately. To do this, you’ll need a policy, some schemas, plenty of coffee, and a decision-making hat.

Capturing Data: How to Build A Data Policy

You want insightful dashboards that inform. You want rich data visualizations that give your sales team the advantage on every sale. But before you can build this awesome reporting and visualization layer, you need to make sure you’re gathering all the right data and that your data is being classified correctly. Often, this involves some complex components like database schemas and architectural design. But, we’re going to set aside the technical IT stuff for now.

Instead, we must start by talking about policies. Data policies are sets of rules you use to collect, store, and apply governance to data. When it comes to data collection and capture, you need to establish policies that inform systems which types of data you want to collect, model, and visualize. Remember, too much data is bad. You want to capture data that’s relevant to your sales teams, dashboards, and mission objectives. Don’t just collect everything and throw it into a lake with grandiose ideas of large-scale data mining and machine learning. That introduces security, governance, and privacy issues into your organization for little-to-no gain. Instead, use the data definitions defined above to build out a policy that ensures the right type of data is being captured.

  • Your data policy should contain all of the following:
  • A guideline for data sensitivity
  • How to capture and ingest data across all data formats (e.g., paper, Salesforce, emails, etc.)
  • A guideline to collect only the data that is needed for performance
  • Role-based guidelines (i.e., who can access what data)
  • Where data is going once it’s ingested
  • How to secure that data at scale (more in the next section)
  • Encryption requirements (more in the next section)
  • Data standardization requirements

Once you’ve built out your policy, roll it into your database schema. So, not only do you have a policy to help employees understand data capture, but you can automate data capture based on this policy in your actual databases.

What’s Next?

You’ve sat down with stakeholders, discussed which sales metrics you need to analyze to achieve your goals, and built out a data policy. Now what? It’s time for storage and maintenance. You know which data to collect, but you need to figure out how to store it, analyze it, and delete/archive it. Stay tuned for post #2 in our data lifecycle management series. We’re doing a deep dive into the ever-tricky and sometimes sticky world of data security, storage, and maintenance.

Are you looking to win with data in 2021? At Delegate, we help firms build robust data pipelines with best-in-class tech stacks. Contact us to learn more.