by Ella Kerr-McCutcheon, Sales Director — Northern Europe, Mediarithmics
Unless you’ve had your head in the sand, it’s impossible to have escaped the past few years without hearing about data technology.
Wouldn’t you agree?
It’s everywhere, promising to be the vital solution that leaders need to cut wastage and increase efficiency across their organisation.
It makes sense, then, that adoption rates have skyrocketed over recent months – bringing the data industry a CAGR of 12.7%.
The Types of Data Technology
Yet, as with every trend, the boom in popularity has delivered a bundle of downsides.
Over this period, the data technology market has started to become overwhelmingly saturated and start-ups are now joining the chaos on the regular. One quick search for data technology online provides an excess of results, with far too many options to choose from.
You’ll find a variety of: Data Management Platforms; Data Lakes; Data Warehouses; Data Clean Rooms; and Customer Data Platforms. Of which, each provider promises to offer unique benefits to their customers, stating that they (and they alone) will be able to give you the competitive advantage you are searching for.
This isn’t actually the case.
Understanding Data Technology
Ultimately, although confusing, the variety is actually something to celebrate. It gives you more opportunities to generate data about your market, customers, and competitors. Understanding the options available to you, then, becomes crucially important.
What are Data Management Platforms?
Historically, this data technology was primarily used to deploy advertising use cases by leveraging 3rd party cookies. Now, as Oracle states, that definition is starting to change. A data management platform “collects, organizes, and activates first, second, and third-party audience data from various online, offline, and mobile sources.” They are most commonly used by advertising and marketing agencies.
What are Data Lakes?
Amazon Web Services dubs data lakes as “a centralised repository that allows you to store all your structured and unstructured data at any scale.” They can prove to be a valuable asset to all enterprise businesses, no matter their size or scale. However, this data technology is frequently adopted by data teams within a business, and not immediately accessible, nor directly usable by other teams in the org (marketing, advertising, product, etc.).
What are Data Warehouses?
An article from IBM informs that data warehouses “pull together data from many different sources into a single data repository for sophisticated analytics and decision support.” This makes this data technology a favourite choice for business analysts, data engineers, and key decision-makers in a variety of different industries. One of the biggest challenges with data warehouses, is as a term, it is used interchangeably with a data lake — creating even further confusion.
What are Data Clean Rooms?
Clear Code defines data clean rooms as “a piece of software that allows brands to run targeted campaigns and measure performance in a privacy-friendly way.” It is most commonly used to enable collaboration between two different parties, like an agency and its client, or a brand and a publisher. Due to its scalability, mega-corporations like Disney or Channel 4 are the ones who most benefit from this data technology.
What are Customer Data Platforms?
Last, but certainly not least, is one of the most versatile data technologies on the market. As Hubspot wrote best, a Customer Data Platform is a software “that aggregates and organises customer data across a variety of touchpoints” — largely working with other products on the market in order to produce exponential and incomparable results.
The Complexity of Data Technology
With so many data technologies within arms reach, it’s easy to get overwhelmed. After all, not only are there different types to compare, but each sector has multiple brands all competing for similar customers.
But what do you choose?
If you really want to open the doors to opportunity and increase the value you could potentially yield from your valuable sales and marketing data, you’ll have to consolidate your choices by:
- Cutting through the noise
- Identifying which services are most relevant to your organisation
- Selecting the best technology for your team
- Onboarding a platform that’s interoperable
The Need to Consolidate Data Technology
If you onboard just one data technology product into your business processes… there are going to be areas that get missed.
Without a system that can combine external and internal sources of data and accurately analyse this information to provide actionable insight, you’ll be missing potential opportunities across your organisation.
Although experts anticipate some collaboration between technologies in the coming years to create services that are more compatible with one another, this is going to take time.
And despite companies like Snowflake slowly starting to diversify their portfolio by expanding from Data Warehouses into Data Clean Rooms, you need a solution that’s already ahead of the curve.
Finally, there is also the danger of having too many tools, where each new solution is layered on top of the previous system, without necessarily connecting well with the rest of the technology stack — leading to more silos and rising costs.
Enter mediarithmics — the convergence of Data Technology
Successful adoption of data technology is impossible without powerful infrastructure in place.
That’s where mediarithmics comes in, offering our next-generation platform as the consolidating answer to all of your problems.
At our core, our next-gen CDP combines the best of:
- Customer Data Platforms
- Data Management Platforms
- Data Clean Rooms
What does this mean for a business leveraging the platform?
With the convergence of marketing and advertising technology, mediarithmics is able to help a business deploy any type of data marketing use case, with any type of data — including 1st, 2nd and 3rd party.
Additionally, with so many platforms available on the market today, the solution offers the ability to reduce reliance on multiple partners, and deploy value across multiple teams — reducing the creation of internal silos.
Of course, there is still the need to be interoperable with other important sectors in the ecosystem (e.g. data lakes, data warehouses etc.), where these platforms will continue to create significant value for more data heavy tasks (e.g. modelling).
We believe that this convergence, or consolidation of data technology will continue to evolve over the course of 2023, to give leaders the best chance at success, providing them with the ability to fine-tune their decision-making through versatile business intelligence.
