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Analysis

An Overview of the 5 Stages of Data Maturity

Data maturity is a measurement of how advanced a company’s data analysis is. The concept looks past the individual titles of employees and considers where data lives, how it is managed and the type of questions being answered with data. Increasing data maturity requires improvement across the entire spectrum of the analytical process — more skilled personnel, better infrastructure, organizational restructuring and a more forward-looking approach to data inquiries.

Here’s an overview of the characteristics of companies at each of the five stages of data maturity. If you want more information about what separates each of the stages and how a company can evolve from one stage to the next, download our Data Maturity Curve guide.

1. Business Reporting

  • Siloed system of record reporting
  • Canned SFDC / Google Analytics reports
  • Merge data in offline spreadsheets rather than a data platform
  • On-demand reports only, no automated reporting
  • Can report on siloed data (Salesforce, Google Analytics, Facebook, Marketo, etc)
  • Business rules are consistent inside a single silo (Salesforce, for example) but not across silos
  • Exclusively deals with backward-looking, descriptive data

This stage is the beginning of any company’s data journey. A lot of mom-and-pop shops and early-stage startups are here. They’ve recognized the need to collect data for their records, but haven’t built any kind of structure to do serious analysis of that data, likely because they don’t need to. Stage 1 companies export their Salesforce or Marketo data and keep it siloed in a series of spreadsheets on a local device rather than blend it together for cross-functional analysis in a data platform.

2. Business Intelligence

  • Standardized datasets
  • Reporting in one place
  • Refresh cadence
  • Sales and Marketing data sources connected, aggregated
  • Business logic lives on the report
  • ETL and warehousing based on data volume
  • Exclusively deals with backward-looking, descriptive data

By Stage 2, companies have blended data together into a single warehouse. The result is a tool that allows them to get a more holistic view of their data and see a bigger picture emerge. The business questions being asked now can go beyond “what were sales last quarter?” to something like “how did the marketing campaigns from last quarter affect sales?” Stage 2 companies don’t have one source for their Salesforce data and another source for the Google Analytics data, etc. They have one source for all of their data.

3. Ad Hoc Analysis/Insights

  • Reporting is sophisticated enough for investigative analysis and rapid model development
  • Data connected across sources, analytics environment abstracted from origin tables
  • Centralized business definitions in a warehouse / model
  • Retain ability to query across modeled and unmodeled data
  • Warehousing is essential at this phase, data lakes are useful
  • Beginning to do diagnostic analysis, moving past recording a spike in data into pinpointing the cause of that spike

As companies advances from Stage 2 to Stage 3, they’re gaining more autonomy in the analytical question-and-answer process. Before, they could access information that their data sources had thought to build answers to, but couldn’t ask their own unique questions. That ability is a hallmark of Stage 3. It’s really important at this point that a company has an independent data team with personnel sophisticated enough to use SQL, Python or R to create their own data models.

At this stage of maturity, it’s natural that companies start to have conversations about data democratization. This isn’t necessarily an important part of the maturity conversation since it doesn’t add new data capabilities or unexpected insights to the overall analysis process; data democratization is just a move to expand the reach of existing insights. The concept of data democratization is an important conversation for every company to have and can be a great way to extend the reach of insights from the first two stages, but doesn’t impact our understanding of data maturity.

4. Hybrid Centralized Data Teams

  • Data landscape is holistic — business rules are versioned and managed
  • Central teams define systems and methods, embedded analysts provide specific value
  • Business model relies on harmonized data across product, sales, success, marketing and ops
  • Data has CXO-level representation and visibility, business-critical
  • Starts to analyze predictive data

By Stage 4, data analysis is sophisticated enough that it is a regular part of every team’s routine operations at a company. The demand for data is high and it’s vital to find a way to prioritize requests to make the most of the data team’s resources. At this stage, there’s a shift in organizational structure to a hybrid model. The centralized data team still exists as a means of collecting information into a single source of truth and building sound data models, but we start to see individual analysts embedded in different business functions who are in charge of answering questions specific to that line of business.

While maturation from Stage 3 to Stage 4 is marked by personnel changes, it also requires a lot more from tools and technology. There are now requirements around governance and engineering that didn’t previously exist. There are now several additional steps in the overall data analysis process, but the result is a flexible, scalable data function that can answer several pressing business questions at once.

5. Predictive Analytics and Machine Learning

  • Business forecasting and planning operates on projected data
  • Online models have product and business ops impacts
  • Offline models used to manage and mitigate negative business dynamics
  • Data lakes are required
  • Regularly using predictive and prescriptive data to make decisions

Stage 5 is for the most cutting-edge data operations. They have the technology and the tools to answer questions that other companies aren’t even considering yet. They’re analyzing information that they’re seeing right now as a way to make decisions about future products, markets, customers, staff, etc. At this stage, companies are investing in more than just how they run their business well, they’re looking at how to make fundamental improvements to the company based on sophisticated data models.

It’s important to note that each stage of the data maturity model takes significantly more time and resources to attain. Becoming more data mature requires a heavy investment in technology and people. They payoff of data maturity is bigger, but the investment takes a longer time to recover. As you become data mature, it’s important to manage your own expectations and be realistic about the value that you’re getting from these investments and when you’ll reap the rewards.

To learn more about the five stages of data maturity, assess which stage your company is in and find out how to advance further, download our Data Maturity Curve guide.

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Adam Bonefeste
Adam Bonefeste is the senior content marketing manager at Periscope Data. When he isn't writing copy, he's probably reading books, running through San Francisco or getting lost in YouTube holes about math/logic problems.