#BI

Clear strategy defines success of digital transformation

In the past 10 years, digital transformation has been quite calm and slow. However, the pandemic has changed this trend by shedding light on the shortcomings of the enterprise technological infrastructure. This situation has forced decision-makers to prioritize IT and increase investment in digital solutions.

Digital transformation is the advanced technologies integration and implementation into business processes. This process is not limited to just hardware or software implementation. It involves profound changes and review of management methods, corporate culture and external communications. Increased employee productivity, high level of customer satisfaction, good reputation and efficient company operations are the result of digital transformation.

In the service industry, digital transformation plays an important role to understand customers and meet their expectations. The transformation process defines digital changes in value formation for products and services development, production, promotion, distribution and sale. Business users can use the generated data to improve customer service and efficiency. This, in turn, makes companies competitive.

The rise of digital innovation has pushed business leaders to implement transformation strategies to reach their target audiences and align their business proposition with customer needs. The right approach will ensure that you understand and build a successful digital transformation framework.

Let’s take a look at a few steps to successfully implement a digital transformation strategy:

Technology advances every day, forcing businesses to quickly adapt to any changes and update their business operations. To be successful and keep up with technological progress, you need to be aware of the possible results. It is important to predict, understand and analyze all changes after the proposed transformation, to understand necessary changes, their results and customer reactions to them.

After goals defining and results prediction it is necessary to select relevant technologies. It is important to understand here that digital transformation is not about mindless investment in advanced technologies. This process means the «right» technologies choosing to help business achieve goals.

Choosing a technology, it is important to answer the following questions:

  1. Does the chosen technology align with your digital transformation strategy and business proposal?
  2. Will the technology rectify operational processes deficiencies after implementation?

The technologies choice is followed by the technical part of the strategy, including the creation of a new digital platform and changes in the operating model.

Digital transformation affects all business areas, both external and internal. The right corporate culture implementing can be a stumbling block in this process. Communication is the key to create a healthy corporate culture. Communication and leaders’ involvement to explain the new vision of business processes, training and consultations allow each team member to quickly accept, understand innovations and be effective.

Digital transformation fundamentally affects business and requires a strategic approach. The technologies being deployed must expand core business capabilities and meet customer expectations. It is important to shift the focus to creating new services and products. To understand customer behavior, market trends, avoid possible losses, it is worth using data and analytics.

Digital transformation can be done easily and painlessly. A well-thought-out strategy and relevant technologies will help you quickly adapt to changes and meet the growing customer needs.

Business Intelligence improves customer service

Customer service is always of immediate interest, and it has a significant influence on business activity generally. With the advent of COVID-19 the whole world was convinced how important customer service is. Almost all offline business had to move activity into online and organize here an effective customer service process. Besides the enhanced online functionality, it required communication clarity and simplicity.

Many companies initially understood that the customer support in «such difficult time» would provide them with increased loyalty in the longer term. Now «pandemic disruption» continues, and business success directly depends on its client-focusedness, customer needs and interest knowledge. It is possible to meet these challenges with the help of BI.

The role of BI in business is invaluable. With the help of BI tools, it is possible to understand deeper: business, customers both their behavior and needs, to make reasoned decisions. By combining several information sources Business Intelligence allows to evaluate the current performance efficiency, identify patterns, define possible future problems and prevent them. According to the Gartner’s forecast BI will grow to $29,48 billion by 2022 and it proves again its value.

5 tasks that can be solved by Business Intelligence:

  1. The single source of truth creating

The modern business has many different information sources including ERP, CRM, website, social media etc. Business Intelligence collects data from all sources and visualizes it. In this case data consumer can easily see and analyze the full picture of customer interaction without manual data checking and collection.

  1. Real-time information receiving

To analyze and make decisions in hindsight is ineffectively. It has an impact on customer relationship, competitiveness and relevance. BI tools provide real-time analytics. With the help of advanced technologies data is transferred from different channels and platforms directly on dashboard. It allows to make rapidly well-founded conclusions and respond to customer behavior changes.

  1. Reasoned decisions making

The main BI task is to eliminate guesswork and enable data-driven decision making. Dashboard data allows to analyze and identify ineffective channels, for example by customer conversions. It allows to perform a change to the strategy, forward the budget and get the best result.

  1. Omnichannel

Customers expect their interactions with a particular company to be known to all employees. Addressing to support team client would like to explain his problem once. Aspect defined that 89% of customers are dissatisfied if they need to repeat information again and again. In this scenario Business Intelligence provides all channels view that allows to monitor the full history of the customer.

  1. Churn reducing

Churn rate is one of the main customer service and business effectiveness indicators. Customer retention is always cheaper than new customers searching and attraction. BI dashboards help define in time problem parts that allows to make changes rapidly for customer retention. For example, increasing the time to solve problems, customer support team restricting or training etc.

These are just a few examples of how BI can improve customer service. Depending on business specification and individual needs BI abilities are unlimited. For more information about Business Intelligence possibilities and benefits please contact DataLabs.   

Innovations that have transformed Business Intelligence

One of the most common questions that business owners ask themselves is «How to be competitive and outperform competitors?» A couple of years back the main characteristics for getting a competitive position were price, design and advertising. Now they are not the only ones. Understanding the client, his needs, willingness and ability to pay are the main characteristics that determine a competitive advantage. Data and BI are too important here because they can help to reply to these questions. The 3rd BI generation release gave a possibility to afford analytics tools to every business user and bring to the light the full data value. The current business intelligence scenario is driven by innovations.

Let’s consider some of them:

  1. Technology shift

Let’s come back to 2000’s. The appearing of a big number of users promoted the appearing of a big data amount. Such situation led to the purchase of high-performance desktops with CPU servers that had bigger memory and direct attached storage. The first 2 generations had a data-oriented stack as a design point. Unlike its predecessors, the 3rd generation is moving towards a network-oriented stack. The first important technology shifts began to emerge during of major vendors IBM and Oracle consolidation. Early BI solutions often were installed on desktops and it was difficult to deploy enterprise software products globally. Over time the Internet became the main design point. In consequence, web-based architecture was developed, and it offered easy installation process and faster deployment options.

  1. Data about data

Realizing that analytics is an enterprise-wide function and not limited to desktop, vendors strived to develop well-managed and secure products seeing the enterprise involvement. Products were built around a metadata layer. A metadata repository stores and manages metadata.

Metadata types:

  1. Storytelling with data

Earlier BI solutions made use tools focusing on reports and dashboards. However, architecture evolvement promoted the functional development of BI solutions. It will not be difficult for a qualified specialist to make evident insights looking at a specific data set. But it’s impossible to say the same about all business participants. Information only in the form of graphs or tables is not always fully understandable for users. For comprehensive understanding of the data meaning, solutions that allow to use the storytelling technique were developed.

5 key BI tool features for successful integration

It’s difficult to imagine decision-making process without data. Current business uses BI tools that can change «rules of play». The main tools’ benefit, besides data analysis, is the ability to make in-depth insights and organize effectively specialists’ work to optimize business activities.

BI tools are designed to help organizations in decisional process. Every tool has its own unique traits and abilities. But there are some factors that promote a successful integration with the company.

  1. Data storage

Regardless of the fact that many companies already have cloud or local data warehouse at the time of BI tool implementation, the presence of a dedicated cloud solution not to come amiss. Such option can massively simplify an integration process for a company that works with limited data warehouse.

Tableau – this platform offers the most limited data storage solution that can be deployed only from Salesforce cloud solution;

Power BI – data storage can be deployed from local servers and using Azure Microsoft cloud solution;

Qlik Sense – this platform allows to deploy data storage from private, public cloud, local servers or three variants combination. The benefit is cloud independence that allows to use different cloud solution.

  1. Embedded analytics

BI tools are often integrated in mature companies with developed and optimized workflows. An ability to implement analytical mechanisms into business applications and systems promotes workflow issues minimization.

Tableau – dashboards can be embedded throughout the workflow, but other metrics and individual values are not available for embedding;

Power BI – almost every qualification user has a possibility to embed different objects. But this tool doesn’t prioritize API, so users can have difficulties with setting up;

Qlik Sense – this tool allows to embed metrics, individual values, numbers, entire and partial dashboards within edge devices, portals and workflows. Another Qlik Sense benefit is API prioritizing that provides free start.

  1. Use cases

Users need to have a possibility to use BI tool on any application or process for any goals and in any context (research, visualization, report generation and other necessary tasks).

Tableau – this tool covers several use cases that are related to visualization;

Power BI – this tool provides more comprehensive offer using Microsoft products;

Qlik Sense – one interface for all use cases allows users to familiarize themselves quickly. Also, it helps to maintain efficiency throughout the workflow.

  1. Data accessibility

Data access doesn’t have to be limited to just a few users. However, the opposite situation when many people can correct the content without proper verification is also unacceptable.

Tableau – a workbook is created by user on his hard drive and after sent to a single server IT double check;

Power BI – authors have self-service access, other users can also explore the content but with limited interactivity. The completed content is sent to a central server for validation and after it is available for circulation;

Qlik Sense – this tool provides end-users with more options. Authors, end-users and other participants between them work on the same server. This enables IT department to validate content of its own and manage content without the intermediate steps.

  1. Work mechanism

BI tools provide deep analysis and understanding with the help of powerful data analysis software. The main goal is to provide the most effective solution that will drive business growth.

Tableau – it works on SQL database using query-based approach;

Power BI – it also works on SQL database and provides solutions from query-based searches;

Qlik Sense – an associative approach is one of the tool’s benefits. Explored data sets are broader, users can identify connections and patterns that can be missed in a typical data engine.

Using BI tools in business activities particularly Qlik Sense, companies get big possibilities for business improvement. They can identify connections and patterns, assess risks and define new development options.

10 up to date BI terms

Man of today is an active user of advanced technologies. Every day each of us deals with different technologies that lighten and improve the life: internet, smartphones, messengers, cloud data warehouse, online banking etc. Business also doesn’t stand apart and implements different technologies to optimize its operative activity. It’s difficult to imagine that all these technologies can get along without analytics. In spite of its not always proper and effective usage, the practice necessity is out of questions and doubts. Data is a valuable company resource that deserves attention and analytics the relevance of which continues to grow. Let’s consider 10 key words of business intelligence 2021.

  1. Embedded Analytics – analytical content integration into business process applications. With the help of this decision there appears possibility to get all necessary information. In addition, users work efficiency and ability to solve complex business tasks improves.
  2. Cognitive computing – this is the trend of the last few years, without doubt. Every day «tons» of data are generated, and work with it is overwhelming for human brains. The main ability of this technology is to process a huge amount of structured and unstructured data and transform it into valuable information.
  3. Data accuracy – effective business decisions can be made using accurate data. This is the first and the main rule of the valid business analytics. Understanding it company management begins to focus more on an organization of the effective data management process. For example, COVID-19 situation has shocked the whole world and affirmed the importance of accurate data ownership. All people wanted to know the truth concerning number of infected, vaccinated, mortality etc.
  4. Decision intelligence – new and important discipline of artificial intelligence age that is oriented to transform information into the best practical solution. This discipline is a complex of data science, social sciences, decision science and management science. Using decision intelligence organizations can improve their operative activity, literacy, project management effectiveness, strategy management and decision-making procedure.
  5. Predictive analytics – is indispensable business tool. Advanced analytics technologies give an ability to forecast event and decisions results that help to prevent risks or mitigate effects. This tool promote business to reach new level.
  6. X-Analytics – not always data analytics relates to numbers. Analyzed business data can be in different types: text, video, audio etc. To gain competitive advantage it is necessary to analyze such data too. X-Analytics sense is to make different type analytics of structured and unstructured data (video analytics, text analytics, audio analytics etc.).
  7. Mobile Analytics – the growth of mobiles usage has instigated appearing of mobile analytics necessity. Mobile decisions implementation and usage in business give many benefits specifically data access in any time and in any location. Also, mobile analytics allows companies to get access to data in real-time mood in any place.
  8. Augmented data management – the task №1 is to provide data security and quality for real-time analytics. But it is quite difficult to make. In this case augmented data management helps to realize it. Gartner’s and Deloitte’s reports affirm this trend and accent benefits that could be achieved by artificial intelligence and data management combining.
  9. Prescriptive analytics – users’ prescribing all possible actions that they have to perform. The main task is to help specialists to choose the correct predictive model for the best decision achievement.
  10. Collaborative BI – is the combination of social media and other advanced technologies with online business analytics. It serves as a powerful tool for business to make rapid and effective decisions.

Focus on human resources determines business success

Many business leaders agree that data is their valuable intellectual property. Meanwhile a considerable part of them either don’t have an exhaustive understanding of its value or don’t know how to arrange data governance correctly. Chaotic investments in advanced technologies implantation don’t solve a problem and sometimes it aggravates the situation.

Don’t forget that data governance is a process but not a software tool. The stuff plays the key role in any processes and specifically in data governance. According to «Big Data and AI executive survey» report 93% of respondents considered people as the biggest obstacle to data governance; 40,3% of respondents cited a lack of organizational alignment as their main problem; 24% of respondents identified cultural resistance as a leading factor in failing to adopt data governance.

Many companies still view data as a byproduct of business processes and they lose possible benefits because of proper analytic resources lack inside company. Only a few organizations use data to diversify business models for effectiveness improvement and new solutions generating. That’s why company management should pay more attention to human resource, education and cultural and behavioral changes.

Why does a human factor have a strong impact on data governance?

This situation is due to the human nature of resistance to any changes. According to business coach Tony Robbins assertion 92% of the 17 million people who try to quit smoking fail; 95% of people who lose weight can’t keep longterm result. Only 10% of people have exact goals and 70% of them can achieve their goals. Furthermore, there are 2 motivating forces for human: to avoid pain and to get pleasure. And the process of learned behavior changing is not always about it.

What solution can be good?

Data governance implementation has to be planned properly and DataLabs team can help to solve this task. Also, it needs to run parallel with the implementation of corporate changing system. System includes common vision for change, strong leadership to communicate this vision, educational strategy for employees and other stakeholders, «plan B» in case of unexpected circumstances and rewards.

Human resistance problems are solved individually in each case. But the important management task is to arrange a favorable environment for employees specifically:

Big Data is changing the healthcare system

Big Data technologies have already exceeded the scope of IT subdiscipline and began to penetrate deeper into different organizations life. And it is difficult to imagine the sector where data could be useless and unnecessary tool. Manufacturing, commerce, finances, education, hospitality, healthcare – this is a partial niche list where data can be used for effective operations. If we’re talking about such important life sector like medicine – Big Data usage is vitally important.

Big Data role in medicine is essential. As expectations in healthcare could become fatal it is critical to get all necessary data on an urgent basis. With the help of Big Data, it is possible to make shorten time for research and results reception that gives an opportunity to develop effective treatment or preventive therapy protocols. Also, doctors have a possibility to get, analyze and give treatment of their patients over a distance.

Let’s consider 3 main Big Data affected areas in healthcare:

1. Research assistance

Currently everybody generates a huge amount of data every day (metadata, text data, video data and location data). All this data is useful especially medical and healthcare data. There are hidden patterns, correlations and relationships. However, it is impossible for human to investigate petabytes of information for logical patterns extract. The life saver in this situation is AI that allows to process big data set, separate all unnecessary and find possible correlations. Big Data plays an essential role in cancer research helping to find answers for questions about cancer treatment, causes if occurrence and mitigating factors that were previously unknown.

2. Changes in insurance

With wearable technologies people can easy monitor heart rate, activity level and sleep cycles etc. This information could be valuable not only for someone but also for doctors, insurance companies and hospitals. Thus, the issue of choosing more suitable insurance in each case could be solved. An open question is an invasion of privacy and personal information usage by insurance companies. Big Data will change the industry, but what restrictions and regulations may be passed remain unseen.

3. Telehealth

Big Data and AI are created for each other and perfectly work together. Without AI it would be difficult to understand, analyze and organize big data. And without big data it would be difficult to develop training data that accelerate AI. Besides help in the doctors’ exploratory activity big data and Ai are used for telehealth applications. Such applications with personal AI assistant give people who have chronic illnesses an ability to get medical advisement and help every day. Also, with the help of these tools it is easier for doctors to process big data that simplify telediagnosis and examination.

Big data makes changes the health care system for better: doctors can detect patients’ condition exactly, get research results on an operational basis and analyze them. Such changes give an ability to continue with an effective treatment keeping to a minimum «guesses» and precious time commitment for them. But collaboration of data scientist and medical and research centers, hospitals and other healthcare companies allows to achieve such results qualitatively and rapidly.

You have a possibility to make the first step to organize the process of data extracting, analyzing and management contacting DataLabs team for advice.

Inmon vs Kimball: who is the winner?

It’s already difficult to imagine a situation where business makes a progress without data. It plays the main role in companies’ operation on the basis of which business team makes different decisions, develops strategies and forecasts etc. For every improving company it is too necessary to make appropriate relations with their data. And the most important step is data warehouse architecture.

What is data warehouse?

Data warehouse is a storage system for data collected from different sources within a company and used to run decision making process.

Currently there are 2 prominent architecture styles to build data warehouse: the Inmon architecture and the Kimball architecture. Ralph Kimball and Bill Inmon propose different concepts. The main difference is a technique of data structure modeling, loading and storage. This difference has influence on the initial delivery time of the data warehouse and the ability to accommodate ETL design changes. However, methods have general characteristics: both of them position the data warehouse as the central data repository for a company; cover all corporate reporting needs; use ETL to load data warehouse.

Let’s consider each method.

 

The Inmon approach

This method begins with the corporate data modelling. With the help of this main subject areas and entities (customers, product/service, vendors etc.) are identified. Consequently, on the basis of this a detailed logical model for each entity is created. Entity structure has a normalized form, data redundancy is avoided as much as possible. It is a key characteristic of this technique that allows to determinate business concept and avoid data update anomalies.

The data warehouse is the one source of the truthful information for enterprise. Such structure simplifies data loading. But it is difficult to use structure for querying because of many tables and joins.

So, B. Inmon propose to build data marts for every specific department (Finance, Sales, Business Development etc.). All data is integrated, and data warehouse is the single source of data from different marts. Such concept guarantees data completeness and consistency across an organization.

Advantages:

 Disadvantages:

The Kimball approach

This approach begins with main business processes and questions identifying. The operating system is a key data source. For data delivery from different sources and loading it into a staging area is used ETL software. From here data is loaded into a dimensional model. The key approach difference is not normalized dimensional model. The star schema is the main concept of dimensional modeling where is centralized store. The fact table contains all data relevant to the subject area. The dimensional table describes stored data. User can make detailing without additional connections as dimensional tables are totally not normalized.  R. Kimball proposes the conformed dimensions concept to achieve an integration in the dimensional model. Key characteristics (customer, product, service) are built once and used by all facts. This guarantees identical characteristic usage by all facts.

Advantages:

Disadvantages:

Both techniques have their advantages and disadvantages and depends on a situation each of them can be more efficient. The main task is to make reasonable and amenable to business needs decision for the best result achievement.

Digital transformation as a driving force for business development

The modern age doesn’t have statical form. Its essence is usual changes. And the one will be successful who acts in unison with them. Firstly, changes were happened in the information technologies area, that became some kind of strength test. Before COVID-19 situation just 15% of company gave priority to the digital transformation. In this case, they could transform their work process to remote format. Other 85% had to work hard for being in time with rapid technology changes and requirements.

The pandemic has global influence and became a reason of accelerated digital technologies implementation. If recently there was a trend to implement artificial intelligence, augmented reality, cloud analytics, DevOps, but after pandemic such customer support tools like chatbots got their popularity.

Information technologies is a trigger of actual changes, after that it became possible to influence the events’ run surgically: to improve efficiency, upgrade some products or services, introduce new business models and many others. International Data Group* conducted research of the IT decision-makers. 32% of them confirmed positive influence of digital transformation on the revenue with 23% increase.

Progressive companies use the algorithm of integration digital project development for their own requirements and take as the most important element for business life cycle. Due to research of International Data Corporation (IDC)**, the amount of investments in this sector will achieve $2 trillion by the year 2022.

The perturbation must be happened with everybody and every system and it doesn’t depend on scope and business activity. The main role belongs to the TOP-management, who has to come first with all these transformations and understand exactly their value. Also, it’s necessary to understand the scope of transformation, because it will be happened in every business levels. Moreover, it could cause changes in corporation culture and business policy generally.

Taking into account each organization’s individuality, it’s not possible to create one and the only decision. And it means need to be ready for experiments and fails during implementation process and searching own «ideal». In addition, it’s important to remember about work group. Every innovation success also depends on clear reporting of «rules of the game» to every employee and favorable working environment. IT and HR-departments are the link between TOP-management and employees and have the responsibility for this part of process.

Transformations are not always about comfort and commonly bring some challenges and losses. Thus, you shouldn’t ignore the strategy. It’s not a panacea, but clear seeing of the picture will help to recognize risks and prevent damages. And the main responsible person is a TOP-manager, who will understand all process steps, be ready to confront with difficulties, and be the main initiator and support for his team.

*International Data Group – American media and research company, focused on the tech landscape.

** International Data Corporation (IDC)  ̶ is an international research and consulting company that investigates world  information technologies and telecommunication market.

We’re being chosen

«Tools developed by DataLabs are extremely impressed. It doesn’t look like QlikView, it looks like a website».

A firm Ansell (Health & Safety provider) hired DataLabs to create an analytics system to gather all its ERPs’ data. They developed and continue to host a system using QlikView to check APIs to have the ERPs’ data in a single dashboard.

Senior manager FP&A, Business Intelligence, Ansell said: «DataLabs knows the technology well, allowing them to build a very strong platform where all data is easily accessible. The tool they developed was extremely impressive. Their team is responsive, open to new challenges, and communicative».

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