The promise of data-driven decision-making has been desired by enterprises of all sizes – and positioned by numerous vendors selling to sales, marketing, and other departments – for many years. The Mar-Tech space alone has grown to over 15,000 tools in a couple of years, and it is still growing. Overall, the results have been very mixed. Enterprises find themselves with many data sets siloed within expensive point tools that are replaced frequently as the search continues for the next best feature set.
As individual teams race to address their day-to-day issues, the company as a whole often misses out on the valuable insights that would come from accumulating data over time and making it available to all departments. With consumer privacy concerns driving changes to traditional sales and digital marketing practices, it’s never been more critical for companies to make strategic and holistic decisions about how they collect, store, and process data.
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As thousands of available tools show, companies are as unique as the people that are a part of them. The majority of tools make wild promises of success, and instant results fail to recognize that it’s difficult for companies to find the best match in a sea of options. In addition to this, the limited use case and cookie-cutter approach that many vendors offer aims for the lowest common denominator. Any evolving enterprise can quickly outgrow them and look towards the next offering, restarting the data collection cycle. Recognizing this wasted motion is the first step towards understanding the importance of building a companywide data set and maturing the company’s data practices.
A world leader in energy-based aesthetic and medical treatment systems worked with Convertiv to develop a first-party data strategy and pipeline. By collecting and storing raw event data at every user touchpoint, they are now able to model and construct custom reports and answer key business questions as they arise.View customer story →
For most tools and vendors, data migration and portability is an afterthought that is reluctantly considered when off-boarding their customers. In addition to being siloed and isolated, the data produced by your company and its customers is sometimes exploited in opaque, behind-the-scenes data reselling schemes. This makes it very difficult to uphold a promise of privacy to your customers in an increasingly regulated climate.
Even the APIs that platforms offer for access to your data usually only expose transformed, report-level data rather than the raw data points that reports are based on. Apples-to-apples comparisons of data sets made available by different vendors are difficult or impossible. This reduces the utility, as you cannot meaningfully compare metrics across data sets. It also conceals all of the assumptions that are baked into the reporting logic. Opaque scores and the use of common terms in unintuitive ways can create misleading assumptions about your business model and customers that will undermine attempts at data-driven decision-making.
How can you evaluate the quality of leads your marketing department generates if you don’t tie it to corresponding sales data? How valuable is a website feature that no paying customers ever saw? Would you spend money on ad campaigns that don’t generate revenue?
Important business questions are rarely siloed within a single team, unlike the data being generated by their activities. De-siloing data is only possible by ensuring that it has the quality, portability, and granularity needed to support the various use cases you will encounter. The best way to deliver these three characteristics at scale is to capture data at its source through the websites, applications, and systems that generate it.
Integrating data across teams will require them to plan and work together. A shared vision of how data will be generated, captured, refined, and stored is necessary. Achieving this may require adjustments to how teams currently perform their activities. It may also require updating how systems are configured so that the data produced is clean and meaningful. Having documentation around what data points are being collected is also very important so that teams know what data is available and the context as to how to interpret the data.
Business intelligence (BI) tools are widely available and can be used to extract value from your data. In addition to the prebuilt reports and dashboards that many tools offer, BI tools can be leveraged to make use of data stored in your warehouse. Since the data is collected in a raw, preprocessed format, reports can be customized to fit specific business needs through SQL and data modeling. By combining both models, you can balance your need for expertise on the team without wasting efforts as your needs become more sophisticated.
When developing a data strategy, it’s important to consider the following attributes.
The road to analytical maturity is one of increasing returns. As the company mines data troves, invests in people over tools, and offers a democratized, single source of truth across all the teams, it enables meaningful insights at an ever-faster pace. It also lays the groundwork for advanced use cases like personalization and machine learning.