Implementing analytics at your company is a multi-team job. Here, we focus on the role of the data team in successfully applying advanced analytics and ensuring that you get the most from your data to make your organization truly data-driven.

Smart organizations already appreciate the power of data and its influence on building successful strategies. Their challenge isn’t to convince decision-makers that they need advanced analytics. Instead, they need to ensure that their organizations benefit fully from all the data at their fingertips and reassure them that they aren’t missing out on capturing and analyzing key pieces of data. That’s why the data team has become so vital. Let’s look at what’s behind the rise of the data team.

More data + more complexity = more FOMO 

You know data is growing quickly every day, but did you know that 90% of all existing data has been generated in the last two years alone, and it’s anticipated that the global datasphere will expand from about 44 zettabytes (ZB) in 2020 to 175 ZB by 2025?

That’s an immense amount of data to capture and manage, and it’s coming from more sources and in more formats than ever before. Every function of every organization generates data in its own way from the various products it develops and tools it uses. This data lives on-premises, in the cloud, or both, existing in an incredibly complex network. If properly analyzed, this data can provide game-changing insights into market dynamics, the customer journey, and product performance.

The problem is that there’s so much of it, and it’s being created so quickly, that it’s difficult to keep track of it all. Only about 9 ZB of this mass of data may actually be stored, and only about one-third of that will actually be used. Data-savvy organizations know that this is unacceptable, because neglected and missing data could contain the key to their competitive edge. 

The solution to their fear-of-missing-out (FOMO) problem is to implement an advanced analytics platform with a comprehensive library of data connectors and expand their data teams to get the most of their data and analytics.

Why build your data team? 

Data teams have risen in importance in recent years because they know best how to work with complex data: capturing, managing, organizing, and integrating it, then finally turning it into powerful strategic and predictive insights that go beyond basic business reporting. With the available data, each business team from any function within an organization can understand what is happening in more granular detail and more accurately predict what will happen and how to get there. 

Using code and software development workflows, specialized data teams can transform vast amounts of complex data into vital information. 

The evolution of the data team

Data teams have evolved from their traditional role of building and supporting self-service BI for a small volume of data. They did this by performing key functions such as: 

  • Data engineering to ensure platform stability and the smooth running of data pipelines that provide good access to data for all
  • Efficient storage of data in data warehouses and/or lakes
  • Data modeling
  • Database administration (when this isn’t already done by managed services)
  • Report and dashboard building for business users
  • Advanced data science functions 
  • Data governance

Today, data teams form a foundational element of startups and are an increasingly prominent part of growing existing businesses because they are instrumental in helping their companies analyze the huge volumes of data that they must deal with. As a result, their role has expanded to include these additional tasks:

  • Defining business rules
  • Establishing a single source of truth
  • Powering embedded analytics
  • Providing ad hoc analytics
  • Surfacing unexpected insights
  • Scaling the use of data and analytics to handle rising data volumes as business operations and functions increase
  • Applying machine learning to data to identify and establish new data and behavioral patterns
  • Anticipating changes, predicting trends
  • Recommending future strategies built on data as a core function

This specialization enables data teams to deliver a level of detail and accuracy that traditional BI tools lack. As such, they can exert strategic influence over a company’s next moves via the data-driven insights that only they can surface.

What can data teams do for you?

Data teams are your key to cutting through the complexity of your business’s challenges. They’re adept at combining disparate sources of data when more complex questions come up. These increasingly difficult questions require sophisticated data models, connected to an increasing number of data sources, in order to produce meaningful answers. As it becomes necessary to handle more and more sources of data to build a clearer picture of your business, it becomes more difficult to anticipate the business’s questions and build a data model that is structured in the right way to answer those questions. 

Therein lies the power of your data team: Armed with know-how, they connect with the end user teams (internal users, product teams embedding insights, etc.) and get to understand their needs so that they can build data models and connect to the data sources that will deliver the greatest benefit to the entire company.

Everyone wins! Product teams use insights derived from data teams’ work to increase user engagement and value while launching new products. Marketing teams can understand exactly what messaging and channels are attracting new customers and predict outcomes for additional investments. Sales teams have a better understanding of current performance and can predict future growth, down to the individual sales rep. Customer success teams can identify behavior, such as high-risk customers who have not yet vocalized discontent, so they can proactively prevent churn. Data teams can even help save lives.

Understanding patient behavior to tackle COVID-19

The need for understanding behavioral patterns has become critical for many organizations since the start of the COVID-19 pandemic, and it has highlighted why data teams play such a vital role in using data to help deliver better products and services to users. 

In the healthcare sector, the pandemic has caused unprecedented challenges in patient care. Scheduling appointments with doctors has been a major challenge, owing to the extra pressure and stress that COVID-19 has put on doctors and their staff.

Luma Health is a digital health company focused on using technology to improve patient access and engagement and to create smarter provider interactions. When the COVID-19 crisis hit, Luma Health’s data science teams used Sisense’s code-driven analytics capabilities to track providers’ interactions with over 10 million patients and discover the best ways for providers to reach their patients.

“In times of crisis, people need insight. The teams at Luma Health were able to use data from Sisense for Cloud Data Teams to analyze, recognize, and address the unpredictable swings in patient behavior that COVID-19 produced, harnessing insights to create a surge of innovation for good.”

   — Aditya Bansod, Co-Founder and CTO, Luma Health

Now, Luma’s data teams can identify patterns in patient-provider communications, scheduling, medical records management, and patient behavior so they can rapidly create messaging for providers that addresses the unique challenges presented by COVID-19 and effectively communicate screening solutions to patients, all without providers paying Luma Health an additional dollar. 

Developing data maturity and anticipating growth

A strong data team, coupled with an advanced analytics platform, enables you to deepen your data exploration as you scale your business and anticipate the increasing complexity that comes with growth.

Data teams deliver fast, accurate business reporting, BI, and data visualizations via SQL-based tools. Advanced languages like R and Python empower them to develop materialized views, create dashboards, set permissions, and perform API quality checks, all of which comprise the next stages of data maturity.

A fully equipped data team will have the means to prepare you for the most advanced stage of data maturity, working in a hybrid centralized environment, with tools such as integration with machine learning (ML) platforms, feature engineering, and text processing in Python, to deliver augmented analytics with ML.

Taking data and analytics to the next level

GitLab is illustrative of a mature analytics program, as a complete DevOps platform delivered as a single application. Built with open source tools, GitLab enables concurrent DevOps, unlocks organizations from the constraints of today’s toolchain, and leverages the community contributions of thousands of developers and millions of users to continuously deliver new innovations.

To help companies operate with maximum agility and efficiency, GitLab’s data team uses Sisense code-driven analytics, hand in hand with Snowflake. This combination has given the team advanced data handling and analytics capabilities. Now they can create previously unachievable reporting, answer critical business questions, and push insights to a new level. The result: The data team has opened up possibilities for discovering fresh answers to complex queries and has identified new insights that enhance users’ experience, understanding, and performance.

The future belongs to data teams

Companies that do all of these things well have invested in data teams that have the ability and capacity to examine all kinds of data and get complex, nuanced answers. The insights that derive from data teams’ work enable these companies to make the best decisions possible.

Nowadays, businesses are built on the ability to ask sophisticated questions and derive clear insights from complex data. No wonder, then, that data — and the data teams’ role in capturing, organizing, and interpreting it — is increasingly becoming an essential part of any organization. Now that data has become ascendant, it truly is time for the rise of the data teams.

SQL, Python, and R

With over seven years of experience in a variety of technologies, former Sisenser Carmen DeCouto is dedicated to empowering advanced data teams as they tackle the next wave of industry-redefining challenges.

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