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Marketers still struggle to build a unified source of data

Marketers still struggle to build a unified source of data

If marketers do not have a unified record of the data, they are oftentimes low on a complex view of their real-time insights, investments, and unification across various teams. 

Nowadays, many marketing organizations are under pressure more than ever before.  Besides, various businesses have to rethink every detail of their business models, driving all marketers to correct strategies according to the situation. In the meantime, marketing organizations and operations teams are struggling to get a sense of their data to evade make horrendous mistakes. 

source of data
source of data

Emily Hoffman, the product marketing manager from Datorama says that it is a well-known fact that their world wavers from periods of clearness to periods of ambiguity. Thus, marketers have to know how to manage both of these extremes. Last month Emily Hoffman and the company’s client, Josh Alvernia, have stepped in a presentation during Discover MarTech to study all the challenges that marketers have to deal with when they have to control and apply data. 

A constant fight between marketers and data

In matters of actionable data, Emily Hoffman believes that marketers have to deal with three major challenges. 

  • The first challenge is related to a lack of a single view among various marketing investments they are working with, the quality of their performance, and their results. 
  • The second one is related to short of insights that can help leverage return on investment and result on a real-time basis. 
  • The third challenge is related to short of management and alignment to push effective cooperation between all teams, regions, and even stakeholders.

Before the outbreak, all of these challenges were already well-known, however, nowadays, they are pushing more than ever while different businesses are coping with huge changes in strategies without a single view of their performance, investments, and return on investments. 

Consequently, marketers are attempting to answer crucial questions related to business. Emily Hoffman made examples with questions like which programs should be saved;  which of them should be forgotten; what messages are the best for engagement with audiences these days. These particular questions are coming to the forefront in a period of uncertainty. 

Excess of data silos

Mentioned above challenges are usually a consequent from siloed and disparate systems among marketing organizations and even the whole business. As a rule, marketing is involved in various channels for communication like social media, paid ads, email, and others without sufficient resources and technologies that combine data from various channel campaigns. In the meantime, teams’ sets for control of all data are split between business intelligence, marketing customer service groups. As a result, everyone works mostly only in their silos. 

Here arises quite a peculiar situation. On one side, marketers are in a dire need for a holistic and clear view to work on the effectiveness of campaigns and understand how their offers and content are doing and what they need to do with engagement. On the other side, they, even more, need a transparent relationship among different teams because it helps to put together stakeholders and make sure that every one of them is interested in their common goal. 

The last report from a Forrester is a great proof of Emily Hoffman’s words. According to it, many business intelligence and marketing teams are quite often siloed to manage patterns of productive communication, even if these teams lean on each other daily operations. 

Assembling everything

To control your marketing analytics, you need a lot of effort to fully de-silo your data. Josh Alvernia, CEO of Clue, states that this a regular thing for his company while they are working with different clients. 

As a rule, it is quite an intimidating task for many marketers who are not acquainted with the process because they use too many platforms. Statistics show that some marketers use about 24 various marketing platforms.  Alvernia’s company usually splits this process to make it more simple and understandable. 

CEO of Clue provided his example to show how his teams work with data on the campaigns to make them more simple. You and your team will have to deal with a process even if you work only on one campaign. You are the one who decides what your company needs from the launched campaign. Moreover, you can speculate on what channels are going to be the most beneficial for your business. Then you make use of those ideas and study the result whether you can try this strategy again or you need to opt for another one. 

Alvernia also added that with the help of fully describing these data sets in 3 categories ( campaign main objectives, channel use, and results), marketers can remove unnecessary data that might intersect. After every detail is going to be at its place, you need to link them. All in all, that is the full process of data modeling. 

Assembling everything
Assembling everything

Alvernia states that data modeling is a mix of a process and the product itself.  The first option applies because it is a non-automatic naming convention that you are going to use while you work on campaigns. The product is also here because you building intelligence software to have a clear picture of the time of receiving, the place to put it, and the actions you will have to do with it. 

The advantage of using data modeling in your campaigns is that you are putting all your data together in an efficient way that provides calculable business results. 

Rely on the data

CEO of clue stressed the simplicity of getting distracted by the inflow of all the possible data among different channels and platforms. To properly develop your data sets that are connected with your goals, your marketers have to possess an extensive knowledge of data that is important against data that do not have enough insight. It would better for you to concentrate on the impact matrix.  This notion means that not everything is not as important as other things. 

As soon as marketers can press the data that will bring a certain result, they can begin to work and prioritize certain platforms that are low-cost and have scale.  When you understand that you have found something you need and that effectively works, you better redouble your efforts on it. 

Above all, you and your marketers have to rely on the data. When the data shows that your assumptions are not correct, pay attention to the data, and continue your search for better solutions. You can mistake only in case when you ignore the proves that data shows you.

The meaning behind the notion of unified data

Unified data links all dissimilar data sources integrally to offer a united view on data of enterprises. On frequent occasions, this term is related to a mix of on-prem and cloud-based data that can be virtualized through a single layer. 

The difference of unified data from a federated data

Federated data is data before a unification or in other words, it is raw data from any amount of archives, that are referenced by a form of adaptive analytics and executed as a combined database with the help of data virtualization. To understand it more clearly, imagine a group of different states that unite in a group of soccer teams or political federation that later came us the Fédération Internationale de Football Association (FIFA). for instance,  if some company compiled a group of databases that can exist on their own, there technology like AtScale’s A3 will help you virtualize the data and then develop a complete one database that you and your team can view. 

The importance of unified data for enterprises

Unified data offers a full, precise, and protected picture of the situation in your business. This happens with the help of analysts and intelligence teams who develop more clear and nuanced analyses.  One of such examples is Rakuten.

importance of unified data for enterprises
importance of unified data for enterprises

Difficulties and certain point for data unification

Data unification can offer a great value to organizations, however, it also can bring immanent challenges in its implementation: security, IT resources and correspondence

Is there a possibility to withdraw from a different data source to unite it? 

There is, however, it comes with certain challenges. The process of extraction, transformation, and loading needs a lot of planning and hours for studying resources by the data engineering team. As a result, it can inform the data warehouse. Security is another important question. The extracted information may have information that can be personally identified. As a result, it has to be detached or there is going to be a risk of compliance violations. 

The process of data unification combined with hybrid cloud environments?

As a rule, all hybrid cloud is combined with on-prem databases and various cloud platforms that are formed within the borders of one company. There are cases when companies pile data on five or even more cloud platforms and some on-prem databases that are or transactional or relational.  If we study it more separately, as a rule, such databases do not credibly describe clients of the company, sales, and other related activities. All in all such activities can not be fully and effectively studied. 

The value of data visualization for data unification

Your first task in building a single view of data is to fully virtualize it. Data virtualization takes into consideration data from a different location, with various data formats and architectures. Then it presents in a single unique view for final business users. Moreover, virtualization helps enterprises to benefit from a unified virtualized data warehouse,  when the data may still be located in the on-prem data warehouses and dissimilar cloud.  Of course, many business users do not call into question the location of the data and how it can be shared among the different data repositories of their company.

Is there only one technology to unite the company’s data?

Of course, there is more than one technology for such a process. There is a mix of self-sustainable data engineering and data virtualization that offers strong adaptive analytics not only for the public but also for a hybrid cloud. 

The possible future of data unification

Experts believe that Artificial Intelligence will take into consideration the scale and speed of the unified data and the hybrid cloud and open up for the new more advanced levels. However, you should remember that:

  • Data always pushes Artificial Intelligence and not the reverse.  In case if source data is being discredited or not full, the algorithms of AI will have inexact predictions;
  • Data analysts and data scientists never fully guarantee the success of Artificial Intelligence.  They have issues with accessing, stabilizing, cleaning, and sorting data into certain business structures that are ready for their AI and BI tools of choice. 

In the long run, unified data will assist organizations to implement more full sets of data into AI and BI tools. As a result, this provides much more clear and full-blown outputs. Remember that even the most advanced tools function when top grade data offers its fuel.

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