How digital analytics has changed in the last 15 years

Over the past 15 years, the digital economy has not only taken hold but now dominates much of our economy.  Generation X, Millennials, and now even Generation Z have shifted much of their purchasing power online.  This has dramatically accelerated the pace of business as vying for these customers requires constant adaptation to shifting digital channels, and the ability to shift execution strategies on a dime.

Digital analytics is more critical than ever before.  Yet digital analytics has become dramatically more complex with the ever-growing number of digital channels and the need for fine-grained targeting and segmentation.

As such, digital analytics has changed drastically over the last twenty years. Let’s explore some key ways it has evolved from the early 2000s to the present and its impact on analytics professionals and processes.

The Need for Speed

In the 2000s, many companies still got their feet wet in the digital economy and new trends were still somewhat slow to evolve.  Companies could afford to let digital initiatives run for a period of time and didn’t have the need to make rapid adjustments.  Teams were more concerned with simply understanding this new phenomenon and react effectively to changes.

15 years later the winners in the digital economy are ones who can answer questions about their effectiveness in digital channels immediately and make adjustments both quickly and effectively.  Management no longer wants to wait a week to get their answers.  If they ask a question in the morning, they want details by the afternoon in order to shape new execution plans for the next day.

This requires digital analytics teams to have the data and analytics assets at their disposal, along with shared knowledge about these assets, to quickly sift through data and find answers quickly.  A system that provides a single plane of glass into an organization’s analytics assets delivers the speed management requires.

Details, Details, and More Details

In the early days of digital analytics, many organizations were happy just to get some view into the performance of their digital efforts and channels.  Top-level digital campaign and sales performance was an easy win to know what was working and what was not working to double down on the winners and eliminate the non-performers.

Three critical things have changed in the past 15 years – the explosion of new digital touchpoints and channels, more detailed behavioral data, and a wider array of demographic information. This gives organizations much more information to micro-target customers in the right channels, which management wants to use to improve performance and reach new customers.

This obviously is not a simple set of rollups.  Digital analytics teams need to bring together all this information from a variety of sources and analyze it to identify and match the characteristics of the channels with the behavior and demographics of the best customers to drive new sales.  But the output will lead to actionable plans for better targeting, higher performance, and increased sales.

Eliminating Silos

We often reference data silos (which still exist and are growing) but what has also changed over the past 15 years is the growth of digital analytics silos.  Digital analytics initiatives often started with a web analytics service and CRM system, but the number of silos quickly grew as each specialized new system or service was to reach the growing array of digital channels.  Add to this the difficulty in marrying digital analytics siloes with traditional analytics.

The inability to coordinate across different siloes and analytics services lead to a number of issues, including:

• The use of different definitions and calculations
• Varying views on the performance
• The inability to create coordinated multi-channel execution plans

Execution in today’s fast-paced digital economy requires consistency, coordination, and speed. Doing so requires analytics teams to break down the data and analytics silos to get a more universal and detailed view of behavior, performance, touchpoints, and execution.  Doing so will also lead to what we discussed in the previous section – a detailed, actionable set of data for high-performing execution plans.

Collaboration and Sharing

Another after-effect of the growing number of analytics siloes is little sharing, re-use, and coordination across digital analytics initiatives.  This typically means that an analyst or data scientist will end up re-inventing the wheel – recreating analytics from scratch that may already exist elsewhere in the organization – resulting in a slower time to insight and duplicative efforts.

By breaking down analytics silos teams can get a more universal view of the analytics that are out there in their organization, collaborate with other teams, and find ways to more effectively share and re-use existing work.  This can dramatically improve the ROI and effectiveness of analytics initiatives.

Collaboration also brings the ability to share knowledge about the various digital analytics assets available such as what the asset is all about, questions it can help answer and now it can most effectively be used.  This not only accelerates the analytics delivery process but also increases the trust in the assets and the answers delivered.

Wrap Up: The Impact of No-Code Data Transformation

The future of digital analytics is incredibly exciting and will continue to evolve in many ways.  Cutting-edge automated data analysis tools and AI have provided digital analysts with greater ability than ever before. As these tools progress in power and use, businesses will continue to find new, revolutionary ways to advance through the use of digital analytics.

Data transformation can be a difficult and complex part of the process when building digital analytics.  You may have solved the simple EL part of the process through data loader tools that can extract data from sources such as Marketo, Hubspot, Google Analytics, and Adobe Analytics and load it into your Snowflake data cloud.  But that’s the simple part.  Transforming this large, diverse, and complex set of data into something consumable in your dashboard is the difficult part.  At least until now!!!

The Datameer SaaS Data Transformation is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake.  The easy-to-use, multi-persona UI, with no-code, low-code, and code (SQL) tools, brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data.  Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance.  Direct integration into Snowflake keeps data secure and lowers costs by leveraging Snowflake’s scalable compute and storage.

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