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 in 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 important for companies to make strategic and holistic decisions about how they collect, store, and process data.
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As the thousands of available tools show, companies are as unique as the people that that are a part of them. The majority of tools make wild promises of success and instant results, yet fail to recognize that its difficult for companies to find the best match in the sea of options. In addition, the narrow use case, cookie-cutter approach aims for the lowest common denominator. Any evolving enterprise is quick to 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 when APIs exist to facilitate access to your data, platforms are usually only exposing transformed, report-level data, as opposed to the raw data points that the 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 correlate across data sets. It also conceals all of the assumptions that are baked into the reporting logic. Opaque scores and use of common terms in unintuitive ways combine to create misleading assumptions about your business model and your 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 useful is a website feature 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 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 charactistics 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 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.
Business intelligence (BI) tools and their operators are widely available and can be used to extract value from your data continuously. In addition to the more established BI tools, there is a new crop of specialized tools available that makes use of data stored in your warehouses rather than trying and capture it their own data. 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 consider the following attributes.
The road to analytical maturity is one of increasing returns. As the company buildsits 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.