Working Toward Frictionless Organization Data — The case for a star-shaped data management and analytics concept.


In most organizations, data exists in silos (i.e., unconnected storage that’s not shared with other teams), causing issues in and of itself.


This is a republication of the article “Working Toward Frictionless Organization Data”, with the title above, highlighting the point in question.


Forbes
Julius Černiauskas
Jun 7, 2022

Julius Černiauskas is the CEO at Oxylabs , a leading proxy networks and data gathering solutions provider.


Edited by


Joaquim Cardoso MSc.
Better Management . Foundation

for better health, care, cost and access for all
Data Health Management —  Insitute
June 23, 2022


Introduction

Data management stands as the new face of process optimization within businesses. 


There are very few companies, especially digital ones, that don’t use any data, as the global trend shows a swift rise upward in the volume and velocity of information.


While there are arguments to be made about whether the volume of data most businesses collect is even necessary, …

… an equally important part of the equation is processing it in such a way that it becomes accessible. 


In most organizations, data exists in silos (i.e., unconnected storage that’s not shared with other teams), causing issues in and of itself.


In most organizations, data exists in silos (i.e., unconnected storage that’s not shared with other teams), causing issues in and of itself.


Structure of the publication


  • Switching To Accessible And Available Data
  • Making Data Understandable
  • Conclusion


Switching To Accessible And Available Data


Making data accessible, however, is not enough. 


Even if it’s processed and delivered to a unified warehouse, it might be tough for other teams to understand, parse and analyze. 

The end goal should be to minimize the friction and waste caused by attempting to integrate data into daily processes.


The end goal should be to minimize the friction and waste caused by attempting to integrate data into daily processes.


Silos are the first roadblock that needs to be cleared until the full power of data can be harnessed. 


While they reduce operational efficiency, finding data silos within an organization is not only unsurprising, it’s almost natural.


Data silos are created as a byproduct of specialization within businesses. 


Teams dedicated to specific processes (e.g., PPC, SEO, development, etc.) set up software that meets their needs. 

Most of that software either directly collects data or indirectly produces it through regular functioning.


Data silos are created as a byproduct of specialization within businesses. Teams dedicated to specific processes (e.g., PPC, SEO, development, etc.) set up software that meets their needs.


As organizations expand, so do their operations. 


The latter, however, often don’t expand only outward but start connecting disparate departments together as greater efficiency can become harder to reach without collaboration.


So, turning silos into interconnected warehouses is often the first step. 


Usually, it requires a team of data analysts and engineers to create such infrastructure. 

It is, however, necessary for any company that has aspirations to become data driven.


So, turning silos into interconnected warehouses is often the first step. Usually, it requires a team of data analysts and engineers to create such infrastructure.


Creating a data warehouse only makes data accessible. 


In some cases, the word “accessible” is used tentatively because other teams outside of the ones managing the warehouse have no or limited access to the infrastructure.


Creating a data warehouse only makes data accessible. Even if (read-only) rights are granted to some individuals and teams, warehouses can be difficult to understand and get used to.



Making Data Understandable


Even if (read-only) rights are granted to some individuals and teams, warehouses can be difficult to understand and get used to. 

There is a lot of labeling, tables and entries occurring. 

Without dedicated and continued work within the system, it will all look overwhelming and can dissuade some people from using it at all.


Expecting teams to extract from and work with data warehouses outside of their regular duties might be a tall order. 


Most of them will be busy with the day-to-day operations necessary to keep the business running.


Expecting teams to extract from and work with data warehouses outside of their regular duties might be a tall order. Most of them will be busy with the day-to-day operations necessary to keep the business running. 

Additionally, data interpretation and analysis are highly skilled activities that require careful consideration.


Additionally, data interpretation and analysis are highly skilled activities that require careful consideration. 


Understanding variance, deviations, correlative/causative factors and many other aspects is necessary since misinterpreting data is probably the easiest thing in the world to do.


There are two ways to solve the issue. 

  • 1.One is to adopt a star-shaped data management and analytics concept.
  • 2.Another solution that is significantly simpler, although less useful in the long term, is — for the data team to work closely with the end data users within each department.

1.One is to adopt a star-shaped data management and analytics concept. 


At the center of all data-related operations will always be the dedicated team. 

They will manage the warehouse, produce dashboards, ensure quality and so on. In turn, all other teams will have someone decently well versed in data analysis who will do all the work.


A star-shaped process has a few immense benefits and a few important drawbacks. 

For the latter, there’s a lot of training and human resource management involved. Not all teams will have someone willing to take up the burden of doing data analytics for their team while also performing the regular duties assigned to them.


It does, however, bring all teams significantly closer to data than any other model. 

While each team gets a person who works with data closely, they also have a closer connection with the entire analytics department. 

The latter is no longer completely separated, allowing them to have a greater impact on daily operations.


Creating such a complicated management process is definitely not for every business. There are many prerequisites that need to be met before a business can undergo such a process.


Creating such a complicated management process is definitely not for every business. There are many prerequisites that need to be met before a business can undergo such a process.


2.Another solution that is significantly simpler, although less useful in the long term, is — for the data team to work closely with the end data users within each department. 


While it is definitely less resource-intensive, it splits off data teams to work more with presentation and visualization, which can detract from their own operations.



Conclusion

Such an approach is completely functional and brings with it numerous benefits. 


Primarily, it works as a great stepping stone for a company to start fostering a data-driven culture. 

For many people, data seems foreign and difficult. 

Having a team of professionals who make it understandable creates ample opportunities for growth.


Many organizations stop at a relatively early stage of their data-driven journey. 


Some decide to simply start collecting huge volumes of data, not thinking about if that accomplishes any important goal down the road. 

Others go a step further and try to process and parse all that data into a single warehouse.


Yet, even that additional step still limits the usefulness of data. It’s only as useful as the people’s will to work with it. 


Thus, making it accessible and processed is not enough. Huge strides need to be made to ease everyone else in the company into working closely with data.


A large part of such a process revolves around demystifying and unburdening the process of working with data. 


Analytics departments can provide the necessary clarity by teaching, training and presenting their knowledge to other teams, all while providing the necessary tools to simplify understanding.


A large part of such a process revolves around demystifying and unburdening the process of working with data. 

Analytics departments can provide the necessary clarity by teaching, training and presenting their knowledge to other teams, all while providing the necessary tools to simplify understanding.


About the author

Julius Černiauskas is the CEO at , a leading proxy networks and data gathering solutions provider.




Originally published at https://www.forbes.com.

Total
0
Shares
Deixe um comentário

O seu endereço de e-mail não será publicado. Campos obrigatórios são marcados com *

Related Posts

Subscribe

PortugueseSpanishEnglish
Total
0
Share