Dispelling Stereotypes Of Digitalization and Data Analytics | by Andrew Bush | Sep, 2022
7 misconceptions that businesses suffer from
In this article, I would like to explore in more detail the top seven data analytics and digitalization misconceptions most businesses suffer from. Let’s dig in!
Unfortunately, a portion of companies in the market still considers “digital transformation,” “big data,” and “business analytics” merely as fancy words. Some underestimate their influence, and some overestimate it. As a result, companies come across serious issues. Largely such a state of affairs is connected to a misunderstanding of the essence of the digitalization processes and management’s stereotypical thinking. In order to change this situation, it’s important to get rid of these core misconceptions, phobias and myths surrounding data analytics.
My many years of experience as an integrator and third-party consultant for projects aimed at digitizing Eastern European companies allow me to distinguish at least 7 ingrained misconceptions that prevent the successful implementation of data management systems, business analytics and digital transformation in general.
“I know better than your reports”
This misconception often occurs with old-school managers. They are used to making decisions personally, guided by their own experience and intuition. They prefer to interpret information received directly from their subordinates in person or during video conferences.
On one hand, if a person is an expert in their business, their interpretation of this data regarding their business works out well for them. They keep up with current affairs, production management, and the context within which their employees make reports and forward information. Most often such a pattern is observed in state institutions and municipal government bodies.
On the other hand, however, the practice shows that such an approach only works so far. The main problem is that instead of reliable evidence and actual data (numbers, values and parameters) a superior receive subjective opinions and evaluations from other people. As a result, the director mostly hears what they want to hear. Furthermore, subordinates do everything not to upset them. They are sensitive to their boss’s expectations, by and large playing along with these expectations by not telling their superiors everything or giving them wrong information, etc.
This is the exact issue that can be solved through digitalization, as it shows the real picture. It’s a lot easier to make correct management decisions based on objective data.
“Once I set up analytics, how to run my business will become clear”
The second misbelief is in essence the opposite of the first. In this case, a decision-maker places his stake exclusively on analytics, data and reports gathered from corporate systems. The leader genuinely thinks that digitalization can solve absolutely all problems in their business and set all things right. This might include understanding what goes on in every department, arranging effective sales systems or planning the entire production program. But this is just an illusion.
In reality, analytics and reporting are merely tools for gathering necessary information from a big volume of data. And here the data requirements set by businesses come to the forefront.
For instance, analytics can help get information on sales dynamics in the company and the rate of conversion at every stage of sales. For example, the number of calls made by subordinates, the number of commercial offers sent, or how many meetings were held and sales handled. All these data are represented in some form — e.g., as a histogram with stages of the sales pipeline. The graph shows that the conversion rate decreases between sending an offer and holding a meeting. This is a useful piece of information, but what does it really expose? How can these data be interpreted? Interpretation is up to a decision-maker. It’s their role to compare facts from a report with the tasks at hand and what kind of a decision they should make. That’s why BI systems are called ‘systems for supporting management decisions’ in Russian-language jargon. Their designation is to provide data.
A minority of specialists in the market think that data interpretation will soon be carried out by standalone, specially-trained neural networks. They will be able to offer specific management decisions to businesses for any given situation. But practice shows that this is naive. Every company is unique and it is impossible to create an automated system for data interpretation. There had been some Russian developers trying to create similar AI systems, however, they never appeared on the market.
“We will build our own analytical system, after all, we have our own programmers”
Often, a director or an IT department supervisor is convinced that their company is capable of building an analytical system on their own without involving a third-party developer or a consultant. This way of organizing work procedures requires active usage of Excel by employees during working hours. The enterprise in question also has a significant number of IT specialists in their staff who “master” (or more likely just study) Power BI or similar tools, and they are ready to carry out an integration process if tasked by their superior. Still, 80% of such projects fail. The remaining 20% are organizations employing a team of IT specialists with quite a unique set of skills (e.g., IT companies, including ones from telecom and fintech spheres) and stagnant enterprises that have a near-zero demand for analytical systems.
In general, a BI system is implemented for the purpose of business development. It helps to conduct experiments over its activities and observe their results (whether they are better or worse). That being said, a system should also evolve along with its company. This can be architecturally, technically, or through a data processing approach (reporting, analytics, prediction).
An analytical system should be designed in the correct way. To begin with, it should be scalable. Developers have to be skilled in modeling, since any business data contain a certain model. From a technical point of view, this model needs to be carefully built into a BI system, data storage should be structured, correct connections should be created, data extraction should be properly arranged, and data should be refreshed, etc. As a rule, such skills are hard to come by among programmers even in larger IT companies.
“We will merge a lot of data from various systems into one database, and all will become clear”
This one is directly related to the quality of data. It’s not uncommon when there is a total mess in a company’s data. For example, the same things (e.g., commodity items and item names) can be named differently in different corporate systems, separate unconnected item master data sources are maintained, etc. With that said, IT directors may still hold the opinion that things will become clear as soon as these segmentary and «raw» data are loaded into a single database, and due to this, reporting will become transparent and high-grade.
As a matter of fact, compiling data from various systems will not solve the main problem. This issue is not technical in essence, rather it is methodological in nature. It’s important to understand if a company runs a unified item master data system in sales, marketing, advertising, and client service, down to printing item price tags in shops. If not, then a unified master data body should be created prior to the stage of analytics implementation, one that will be a source for standardized item names in all other corporate systems. I can tell from my experience that managing master data and regulatory reference data is quite a hot-button topic in the Russian business world. Without solving these issues no BI system will work.
“Analytics is expensive and I need to invest a lot of money at once”
Quite often there is the opinion in companies considering digitalization that implementing analytical systems is a luxury. In part it is. In fact, integrators themselves use every trick in the book in order to maintain a high project price, explaining it away with high labor input in the course of studying the inner processes of their client’s enterprise. They examine sources of data, rules of storage and extraction, compare item names, calculate formulas, etc. After that, clients are bound to start being afraid of making investments in this direction, as they have no clear picture as to their demands.
It’s possible to build some basic business analytics without establishing a standalone IT infrastructure and without outside help by single-handedly extracting data from corporate systems. In this case, expenses will be a whole lot lower. All the while, the job can be done by business analytics and department supervisors. If the necessity arises, they can request help from IT specialists in developing automated tools for data extraction. One can also adjust the analytical work based on these tools (platforms, visualization, dashboards, etc.) For companies making their first steps in the BI sphere, it is a beneficial experience and a very cost-effective solution. Besides, their specialists can examine the data by themselves, and understand what requirements they have for an analytical system. Once this is done, they may consider a full-scale infrastructure.
“The benefit of utilizing analytics consists in labor cost saving”
As a rule, many digitalization projects are connected with automating existing reports and eliminating manual labor, all the while requiring data analysis. And quite often IT directors insist on evaluating the economic benefit from integrating a new system in terms of labor costs saved. But really, a BI-system influence is not limited to reducing payroll bills. It’s just the tip of the iceberg.
An analytical system provides new opportunities for business thanks to making data accessible, finding consistencies, and creating unexpected insights. It helps a decision-maker to work out fundamentally different management decisions regarding their qualitative aspect. For instance, they can choose suitable cities to open branches in or replace face-to-face employees’ meetings with online video conferences. And quite possibly these improvements will show a significantly larger economic benefit for the company than a mere labor cost reduction gained by simplification of preparing reports. As a result, a business may start making a lot more profit and get a boost for further growth.
“I will hire an integrator and they will provide me with the best methods of data management”
Quite a common error among clients is completely not participating in the process of development and integration of an analytical system. In this case, an enterprise entirely relies on the expertise of a chosen system integrator. This way, they just compose a generic technical specification and take no part in the further stages of the project.
In practice, it often turns out that a developer in this case has no clue about the bare essentials and principles of solving their client’s tasks, they just see an opportunity to earn money. As a result, the contractor will just conduct experiments at the client’s expense, and it will affect the quality and the success rate of projects. No less importantly, a team must be experienced in the subject area. It’s insufficient to just be capable of building BI systems using a template. It’s important to have expertise in the relevant areas. Each industry has its own well-established practice, its own set of tools, and its own connected systems (e.g. SAP is used as ERP), so the contractor should have a good understanding of these aspects.
For this reason, a client must closely monitor a developer’s work, and if the parties start to have discrepancies, and their dialogue becomes purely formal, there will be no chance of success.
To wrap up, if you want your business to succeed I would encourage you to not underestimate the power of data analytics and the entire digitalization process.
Got questions? Don’t hesitate to reach out at [email protected]
7 misconceptions that businesses suffer from
In this article, I would like to explore in more detail the top seven data analytics and digitalization misconceptions most businesses suffer from. Let’s dig in!
Unfortunately, a portion of companies in the market still considers “digital transformation,” “big data,” and “business analytics” merely as fancy words. Some underestimate their influence, and some overestimate it. As a result, companies come across serious issues. Largely such a state of affairs is connected to a misunderstanding of the essence of the digitalization processes and management’s stereotypical thinking. In order to change this situation, it’s important to get rid of these core misconceptions, phobias and myths surrounding data analytics.
My many years of experience as an integrator and third-party consultant for projects aimed at digitizing Eastern European companies allow me to distinguish at least 7 ingrained misconceptions that prevent the successful implementation of data management systems, business analytics and digital transformation in general.
“I know better than your reports”
This misconception often occurs with old-school managers. They are used to making decisions personally, guided by their own experience and intuition. They prefer to interpret information received directly from their subordinates in person or during video conferences.
On one hand, if a person is an expert in their business, their interpretation of this data regarding their business works out well for them. They keep up with current affairs, production management, and the context within which their employees make reports and forward information. Most often such a pattern is observed in state institutions and municipal government bodies.
On the other hand, however, the practice shows that such an approach only works so far. The main problem is that instead of reliable evidence and actual data (numbers, values and parameters) a superior receive subjective opinions and evaluations from other people. As a result, the director mostly hears what they want to hear. Furthermore, subordinates do everything not to upset them. They are sensitive to their boss’s expectations, by and large playing along with these expectations by not telling their superiors everything or giving them wrong information, etc.
This is the exact issue that can be solved through digitalization, as it shows the real picture. It’s a lot easier to make correct management decisions based on objective data.
“Once I set up analytics, how to run my business will become clear”
The second misbelief is in essence the opposite of the first. In this case, a decision-maker places his stake exclusively on analytics, data and reports gathered from corporate systems. The leader genuinely thinks that digitalization can solve absolutely all problems in their business and set all things right. This might include understanding what goes on in every department, arranging effective sales systems or planning the entire production program. But this is just an illusion.
In reality, analytics and reporting are merely tools for gathering necessary information from a big volume of data. And here the data requirements set by businesses come to the forefront.
For instance, analytics can help get information on sales dynamics in the company and the rate of conversion at every stage of sales. For example, the number of calls made by subordinates, the number of commercial offers sent, or how many meetings were held and sales handled. All these data are represented in some form — e.g., as a histogram with stages of the sales pipeline. The graph shows that the conversion rate decreases between sending an offer and holding a meeting. This is a useful piece of information, but what does it really expose? How can these data be interpreted? Interpretation is up to a decision-maker. It’s their role to compare facts from a report with the tasks at hand and what kind of a decision they should make. That’s why BI systems are called ‘systems for supporting management decisions’ in Russian-language jargon. Their designation is to provide data.
A minority of specialists in the market think that data interpretation will soon be carried out by standalone, specially-trained neural networks. They will be able to offer specific management decisions to businesses for any given situation. But practice shows that this is naive. Every company is unique and it is impossible to create an automated system for data interpretation. There had been some Russian developers trying to create similar AI systems, however, they never appeared on the market.
“We will build our own analytical system, after all, we have our own programmers”
Often, a director or an IT department supervisor is convinced that their company is capable of building an analytical system on their own without involving a third-party developer or a consultant. This way of organizing work procedures requires active usage of Excel by employees during working hours. The enterprise in question also has a significant number of IT specialists in their staff who “master” (or more likely just study) Power BI or similar tools, and they are ready to carry out an integration process if tasked by their superior. Still, 80% of such projects fail. The remaining 20% are organizations employing a team of IT specialists with quite a unique set of skills (e.g., IT companies, including ones from telecom and fintech spheres) and stagnant enterprises that have a near-zero demand for analytical systems.
In general, a BI system is implemented for the purpose of business development. It helps to conduct experiments over its activities and observe their results (whether they are better or worse). That being said, a system should also evolve along with its company. This can be architecturally, technically, or through a data processing approach (reporting, analytics, prediction).
An analytical system should be designed in the correct way. To begin with, it should be scalable. Developers have to be skilled in modeling, since any business data contain a certain model. From a technical point of view, this model needs to be carefully built into a BI system, data storage should be structured, correct connections should be created, data extraction should be properly arranged, and data should be refreshed, etc. As a rule, such skills are hard to come by among programmers even in larger IT companies.
“We will merge a lot of data from various systems into one database, and all will become clear”
This one is directly related to the quality of data. It’s not uncommon when there is a total mess in a company’s data. For example, the same things (e.g., commodity items and item names) can be named differently in different corporate systems, separate unconnected item master data sources are maintained, etc. With that said, IT directors may still hold the opinion that things will become clear as soon as these segmentary and «raw» data are loaded into a single database, and due to this, reporting will become transparent and high-grade.
As a matter of fact, compiling data from various systems will not solve the main problem. This issue is not technical in essence, rather it is methodological in nature. It’s important to understand if a company runs a unified item master data system in sales, marketing, advertising, and client service, down to printing item price tags in shops. If not, then a unified master data body should be created prior to the stage of analytics implementation, one that will be a source for standardized item names in all other corporate systems. I can tell from my experience that managing master data and regulatory reference data is quite a hot-button topic in the Russian business world. Without solving these issues no BI system will work.
“Analytics is expensive and I need to invest a lot of money at once”
Quite often there is the opinion in companies considering digitalization that implementing analytical systems is a luxury. In part it is. In fact, integrators themselves use every trick in the book in order to maintain a high project price, explaining it away with high labor input in the course of studying the inner processes of their client’s enterprise. They examine sources of data, rules of storage and extraction, compare item names, calculate formulas, etc. After that, clients are bound to start being afraid of making investments in this direction, as they have no clear picture as to their demands.
It’s possible to build some basic business analytics without establishing a standalone IT infrastructure and without outside help by single-handedly extracting data from corporate systems. In this case, expenses will be a whole lot lower. All the while, the job can be done by business analytics and department supervisors. If the necessity arises, they can request help from IT specialists in developing automated tools for data extraction. One can also adjust the analytical work based on these tools (platforms, visualization, dashboards, etc.) For companies making their first steps in the BI sphere, it is a beneficial experience and a very cost-effective solution. Besides, their specialists can examine the data by themselves, and understand what requirements they have for an analytical system. Once this is done, they may consider a full-scale infrastructure.
“The benefit of utilizing analytics consists in labor cost saving”
As a rule, many digitalization projects are connected with automating existing reports and eliminating manual labor, all the while requiring data analysis. And quite often IT directors insist on evaluating the economic benefit from integrating a new system in terms of labor costs saved. But really, a BI-system influence is not limited to reducing payroll bills. It’s just the tip of the iceberg.
An analytical system provides new opportunities for business thanks to making data accessible, finding consistencies, and creating unexpected insights. It helps a decision-maker to work out fundamentally different management decisions regarding their qualitative aspect. For instance, they can choose suitable cities to open branches in or replace face-to-face employees’ meetings with online video conferences. And quite possibly these improvements will show a significantly larger economic benefit for the company than a mere labor cost reduction gained by simplification of preparing reports. As a result, a business may start making a lot more profit and get a boost for further growth.
“I will hire an integrator and they will provide me with the best methods of data management”
Quite a common error among clients is completely not participating in the process of development and integration of an analytical system. In this case, an enterprise entirely relies on the expertise of a chosen system integrator. This way, they just compose a generic technical specification and take no part in the further stages of the project.
In practice, it often turns out that a developer in this case has no clue about the bare essentials and principles of solving their client’s tasks, they just see an opportunity to earn money. As a result, the contractor will just conduct experiments at the client’s expense, and it will affect the quality and the success rate of projects. No less importantly, a team must be experienced in the subject area. It’s insufficient to just be capable of building BI systems using a template. It’s important to have expertise in the relevant areas. Each industry has its own well-established practice, its own set of tools, and its own connected systems (e.g. SAP is used as ERP), so the contractor should have a good understanding of these aspects.
For this reason, a client must closely monitor a developer’s work, and if the parties start to have discrepancies, and their dialogue becomes purely formal, there will be no chance of success.
To wrap up, if you want your business to succeed I would encourage you to not underestimate the power of data analytics and the entire digitalization process.
Got questions? Don’t hesitate to reach out at [email protected]
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