Companies today are generating vast amounts of data. To manage all this data and put it to good use, you need a data strategy.
A data strategy framework outlines best practices for data management, data security, and data integrity. Based on three pillars (people, processes, and technology), it dictates how a company should handle its data.
Data strategies are tricky to implement as they require intense planning, communication, and collaboration.
We’ve outlined ten best practices designed to set your strategy up for success.
But first, let’s look at what a data strategy is and what we mean by “data”.
What is “Data”?
Going back to basics: “data” is simply information or pieces of information.
Data is stored on your desktop, in the cloud, or on an external hard drive. If you use spreadsheets, congrats — you’ve already worked with data!
Modern businesses handle all kinds of data: customer, transactional, performance, financial, etc.
From a small business tracking its stock levels to a large corporation analyzing industry data to predict market trends, data is everywhere.
Smart business owners know how to leverage data for strategic insights. But for data to be usable, it must be high quality, well maintained, and securely stored. This is where a data strategy comes in.
What is a Data Strategy?
According to Gartner, a data strategy is “a highly dynamic process used to acquire, organize, analyze, and deliver data in support of business objectives”.
Essentially, a data strategy is a set of guidelines and principles defining how your business should manage its data. It outlines the people responsible, the processes to follow, and the tools to use.
What is the purpose of a data strategy?
The purpose of a data strategy is to help an organization solve data challenges, remove friction, and ensure data is used effectively to support business goals.
The data strategy guides all data-related activities, instructing team members on best practices for managing and interpreting data.
A data strategy also ensures data quality so teams can rely on data to answer business queries.
You should always approach data with the outlook “I’ve got a question and I’m going to look for the answer in this data”, rather than “I’ve got all this data. What should I do with it?”
In sum, a well-defined data strategy serves several purposes, including:
- Driving business goals
- Maintaining data quality
- Improving inefficient data processes
- Answering business questions
- Highlighting new opportunities
- Optimizing acquisition channels
- Reducing costs related to data storage and maintenance
- Understanding customer preferences and behaviors
10 Data Best Practices for a Winning Data Strategy
These data best practices address people, processes, and technology.
1. Make data a company-wide priority
A successful data strategy is one that has everyone on board. People need to know about it and be willing to adopt it.
In this case, data needs to be seen as a company value and strategic asset. How do you make that happen?
By creating a data-first culture. And there are a number of steps to achieving this.
- Designating a “champion” or executive leader to advocate for the data strategy and related initiatives
- Getting buy-in across different levels of management, from the C-suite down to team leads. A data strategy needs to be supported and prioritized by all stakeholders.
- Defining common definitions, principles, and metrics across the company, organizing internal training where necessary
- Implementing cross-functional, transversal data processes with one single source of truth
- Democratizing data by making it accessible to non-tech teams (e.g. investing in a no-code data extraction tool for sales and marketing)
2. Encourage a data-driven mindset
To be data-driven is to instinctively turn toward data for answers and take decisions based on what the data says.
Data needs to be the go-to for answering questions. How long does it take for customers to churn? What caused this peak in acquisition? Why are our customers switching to a freemium alternative?
There’s a misconception that you need to be mathematically or technically minded to be data-driven. You don’t. You just need to understand why data is relevant and be able to draw conclusions and insights.
If it’s not already the case, get team members in the habit of backing up their decisions with numbers. The first reaction to any challenge or new idea needs to be: “What do the numbers say?”
3. Track KPIs and metrics
In a data-first organization, everything is measured. The best way to do this is to match your strategic goals with Key Performance Indicators (or KPIs).
There’s no point working with data if you’re not going to track key figures. This doesn’t mean measuring all kinds of vanity metrics, but at least focusing on the most critical ones. For SaaS businesses, this is usually MRR, Churn, and LTV.
KPIs are typically assigned to teams and individuals but key projects and initiatives should also have measurable targets.
The OKR framework (Objectives & Key Results) works well with KPIs because it forces you to consider the KPI as part of a long-term goal. This makes you focus on the “why” behind the numbers.
You can also have metrics related to data quality. Tracking data quality lets you:
- Measure the effectiveness of your data strategy
- Understand how accurate your data is
- Highlight missing, incomplete, or inconsistent data
- Take corrective actions to improve data quality
4. Don’t go chasing the latest tech trends
There’s a lot of hype around data management best practices technologies. But that doesn’t mean you have to buy into every new trend.
In reality, you probably don’t need Big Data or AI. In fact, most companies aren’t even doing Big Data or AI. Trends and buzzwords cloud our vision of what a good data strategy should look like.
A data strategy is just as effective as a simple tech stack. The key to success lies in the process and data quality rather than the tech.
Take one of our corporate customers as an example. They wanted to extract market data to analyze their US market share. Despite only extracting from 20 websites, it was enough to power their internal analytics tool without the need for more complex technology.
What’s even more important than the tech or volume of data is the metadata (i.e. the description of the data). You need metadata to make sense of raw data. So forget the trends and focus on the processes and metadata instead.
5. Find the right people to drive the data strategy
Your data strategy lead doesn’t have to be a tech person. You just need someone who knows how to navigate between teams, make data-driven decisions, and understand how data ties in with business goals.
A product manager at the intersection of sales, marketing, and tech could be your ideal candidate. However, if your company is looking to enhance its data strategy with external expertise, consider hiring a data consultant.
You also need to document the roles of other stakeholders. Who’s responsible for what? Depending on the company size, this could involve data engineers, data scientists, data analysts, and business managers.
Within your data strategy framework, it’s best to define data governance guidelines, a data auditing process, and assign a data steward in each department.
A data strategy will likely introduce more data analysis and tools. Are people skilled enough in this area? Do you need to hire more analysts? Internal training and hiring are points to consider.
6. Establish watertight data security processes
Data security concerns everybody. It’s not just something for IT, legal, or your external Data Protection Officer to deal with. If you don’t control data security by design, you never will.
The GDPR, a data protection act for European citizens, requires organizations to be transparent about how data is stored, who has access, and what it’s used for.
The more data that’s shared across teams, the more you should be careful about how it’s distributed and who has access. People should only have access to the data they need.
For example, there’s no reason developers need access to detailed customer data or sales reports.
Out of respect for customers, data security needs to be a top priority when designing how data is shared between teams and systems.
7. Find the right tool and techniques for data analysis
Everyone should be able to examine and draw conclusions from the datasets that concern them. Whether it’s a product manager looking at user data or a sales rep examining lead data to identify buying signals.
Successful data analysis requires the right techniques and visualization tools (and the training to use them).
Different analytics techniques include predictive analytics, text analytics, cohort analysis, cluster analysis, and sentiment analysis, to name a few.
The best analysis techniques are the ones that give strategic insights while still respecting your data governance and privacy policies.
Certain use cases involve personally identifiable information and need to be treated carefully. Not all analytics techniques are lawful and internal teams need to be aware of this.
8. Establish a Single Source of Truth
When data gets shared, the data lineage can become convoluted as it passes through teams and systems. This makes it hard to trace and verify, which raises doubts about the quality and accuracy.
Rather than having siloed data across teams, you need a Single Source of Truth (SSOT) that everyone can trust. The SSOT concept ensures that everyone bases their decisions on the same consistent and accurate data.
Why does it matter? Because critical business decisions are based on data. And if that data is wrong, this could result in a loss of earnings or other costs.
SSOT is therefore crucial to maintaining an organization’s data quality standards.
So how do you ensure data lineage? One approach is to create a pipeline that ingests raw data from different sources and replicates it into a single repository for storage.
Before storage, you may want to transform it into a format that’s easier to analyze. Your data governance process should outline where the data originated from and how it was transformed.
9. Increase productivity with data automation
Data automation is the process of extracting, transforming, and storing data using automated tools (rather than doing it manually).
Data automation is a huge time-saver for teams that rely on sourcing web data, such as sales reps when researching leads and opportunities. Data automation can be used to
- Create a list of qualified target accounts
- Build an automated lead-gen machine
- Enrich a CRM with fresh data or score leads
- Identify market trends and buying signals
To automate data sourcing, use a data automation tool that extracts data then transforms and loads it to your CRM for analysis. Such tools are usually powered by web scraping.
When automating data for non-technical teams, it’s best to avoid APIs, scripts, and complicated tech. Use a no-code automation tool like Captain Data instead.
10. Enrich existing data with web data
Whether it’s a large enterprise or startup, internal data is never enough to drive outbound sales and marketing. CRM data needs to be completed with information from the internet, especially for lead generation.
If the lead data in your CRM is old or missing information, you can refresh and fill in the gaps with web data using data enrichment.
This gives more context to potential opportunities and increases the chance of closing a deal through personalized outreach and targeting (as used in account-based marketing).
Your best bet is an automated data extraction tool like Captain that gives easy access to web data. You decide which websites to target, what data to extract, as well as how to aggregate data from multiple sources.
Bonus Tip: Continuously Assess and Improve Your Data Strategy
Periodically review your data strategy to make sure it’s doing its job and serving business needs.
Not getting the results you need? Here are a few things you can try.
- Test new tools. Your current software doesn’t do exactly what you need it to? Try an alternative. Tools should complement rather than contradict each other so if there’s a clash in the tech stack, consider changing.
- Investigate errors and loss of data quality as they happen. Don’t let them go unfixed.
- Make data sourcing more efficient by testing and prioritizing the data sources and data points that are most relevant to the business strategy
And that’s a wrap! For more tips on getting the most out of data, check out the Captain Data blog.
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