Gone are the days when analyzing and visualizing data to get information was a job that was limited to the IT and business intelligence (BI) divisions. Gone also are the days when the sole possession of knowledge, skills, and tools for data processing was in the hands of the “data guy.”
Data is becoming more and more abundant and essential for various business operations. This makes centralizing data processing on one or two divisions an inevitable bottleneck. On the other hand, analytics and visualization tools are becoming easier to use, with more intuitive user-friendly interfaces that require less and less technical expertise.
What SSBI Is About
Self-service business intelligence (SSBI), also called self-service data exploration, has become an important approach for data-driven insights in business. It means giving the ability to the wide range of employees who are not experienced with data to drive insights from relevant datasets and create exploratory visualizations to help them better understand the data and to use it in reports. It’s also a part of what is called data democratization if you’d like another fancy term on the plate.
It should be, however, distinguished from the second approach called dashboarding. While the latter should still be the responsibility of the experienced BI team, turning amounts of data to finely curated reports on the most important KPIs within a well-developed narrative can happen. The SSBI approach aims to:
Avoid time delays in data-driven decision making among the low and mid-level teams that may happen due to the centralization of analytics responsibilities.
Minimize intuition-based decisions that can be made by low and mid-level teams on a daily basis due to lack of analytical capabilities.
Enhance internal communication within the teams by making data-driven insights and visualizations easier to generate, and therefore more frequent integration of reports.
Enhance external communication of the organization as the insights and visualizations can also be easily used in developing publications, like blog posts for example.
Google Sheets and Datawrapper
There are tons of visualization tools out there that can enable you to create an SSBI system for your organization, some of which are technologically advanced, but each has its best uses and downfalls.
Just like Google Sheets and Datawrapper. The advantages of using these tools are the following:
– Businesses with no capabilities of experienced teams or infrastructure can implement the system.
– Anyone can use it as it requires little to no technical expertise.
– Visualizations can be easily duplicated and edited, suiting fast-based work routines.
– Visualizations can be easily well-formatted and laid out, leading to efficient reporting.
– Generate both interactive and static visualizations that are suitable for embedding in various forms of reports, from web-based all the way to paper-based.
Using a self-service BI solution can help streamline operations and support critical decisions. It also encourages collaboration, simplifies daily business needs, and increases one’s competitive advantage. With the efficiency brought by SSBI, businesses can focus on what matters most to them.
Want to understand how visual representations can support the decision making process and allow quick transmission of information? Sign up for The KPI Institute’s Data Visualization Certification course.
Digitalization is nothing new in the business industry as the world has shifted toward digitalization for the past few decades. However, the Covid-19 pandemic has catapulted the digital model of business to another level.
In a 2020 study, Salesforce showed that 60% of customer interaction took place online compared to 42% in the previous year. Meanwhile, up to 88% of customers also expect digital innovation from companies during and after the pandemic. This shows how customers start to put emphasis on company value by what they are seeing online. The sudden surge of the online presence of the majority forced businesses to rethink their existing strategy, especially when it was directly related to their customers.
The changes brought by digitalization
The increasing use of digital-based platforms has affected several aspects of businesses. Demand to be available digitally has changed the marketing industry even before the pandemic hit. We can easily spot how large to small companies transitioned their marketing strategy into a digital approach. Even though it sounds like most companies are already familiar with digital marketing, the fast-changing nature of it requires constant learning on what is relevant at the moment.
The second change mostly catalyzed by the pandemic is the change in how companies do their business. Many employees have been forced to work remotely and moved most of their workflow online. Occasionally, companies have been required to modify their products or services to fit the current demand or trend.
The adaptation of businesses on their strategic planning and performance measurement to fit the ongoing and upcoming challenges is a conversation that is often missed. The fast-changing digital world has caused a lot of developments in companies towards important matters that can sustain their business by upgrading and preparing their resources.
Innovation is the key for digital sales
Similar to other sectors, sales activities also demand to have a digital model more than ever. Data shows that digital sales, in general, can boost revenue up to 28%. As much as digital sales sound promising, it also demands a constant upgrade and innovation.
Innovation is one of the most crucial parts to achieving maximum digital sales growth. Just like traditional sales, the ability to engage with the customer is still a major factor in the success of sales. However, the digital model demands companies to be more attentive to the changes in customer behavior. Companies and even salespersons are required to see the need and trends in the market.
The innovation in sales technology is also predicted to have a big impact on how long-term revenue is generated. The use of more efficient CRM and even the use of AI can be a huge booster in sales growth. For example, now the customers have become more digitally savvy, this also means that they are more aware of cyber security. Things such as transparency in sales activities and data collection are just some of the things they look out for. In turn, the growth in technology would also mean an increase in demand for people who are knowledgeable in the digital space and can operate the business.
In early 2020, mobility restrictions and lockdowns were implemented in most countries due to the spread of COVID-19. Those restrictions clearly impacted the way people interact and communicate. Online and virtual meetings became the new normal, from schools to businesses.
In the realm of corporate governance, one thing that changed was the shareholders meetings or annual general meetings (AGMs). AGMs are a critical component in corporate governance practice that is mandatory by law, where directors and shareholders interact. The pandemic forced companies to shift from traditional AGMs to virtual shareholders meetings (VSMs), and this brought technical, legal, and cultural challenges.
A report from the World Bank claims that 84% of the economies are allowing VSMs in their legal frameworks. Even countries that previously did not allow VSMs had introduced provisions through emergency legislation to enable virtual meetings. VSMs, as a virtual meeting, clearly have some differences from a conventional physical meeting.
In the 2020 report of VSMs practice, several benefits and drawbacks of VSMs are reported. Most companies expressed positive reactions due to its lower cost of operation and being more eco-friendly, while some companies expressed the technicalities of organizing VSMs are quite an issue. Conversely, shareholders felt that there might be a lack of transparency and felt less involved in the company. It can be argued that a lack of readiness and experience of companies to organize VSMs are the cause of the negative response from some of the shareholders.
A year later into the pandemic, mobility is slowly increasing and borders are starting to reopen, however, some changes are likely here to stay. As a consequence, there would be a future for VSMs as a permanent substitute for traditional AGMs. Best practices are starting to be built to successfully replace AGMs. Below you can find some recommendations based on these best practices in organizing a VSMs:
Establish the rule of conduct of the meeting just as a physical one, and communicate them clearly: In a simple way, you should treat the VSMs as a face-to-face meeting. Communicate them clearly in plain English. Specifically for VSMs, you should differentiate between regular participants and verified shareholders, whether they have the permission to speak or they can only listen.
Establish clear procedures for verified shareholders to vote remotely: Make sure that you only give vote access to the shareholders that have voting rights. It is also important that the votes are properly recorded.
Establish clear procedures for shareholders to ask questions: Questions are important, so make sure the Q&A sessions are scheduled. For questions that cannot be answered at the meetings, document them and answer them later (e.g. via e-mails, phone calls).
Archive the meeting and make it publicly available for a reasonable period of time: Most virtual meeting platforms have a record feature, which you can easily use to do this archiving task. Having someone assigned to prepare the Minutes of Meetings can also be useful.
Companies should take this moment as an opportunity to improve their relationship with stakeholders and shareholders. This transition from conventional to virtual meetings opens up many possibilities. The flexibility that is enabled by technology has allowed solutions that were previously impossible. It is possible that VSMs are only just the beginning of a better form of future AGMs.
We deal with data every day, especially at work. It can fuel our decisions and change the way we work. At the same time, if we’re surrounded by a huge amount of data, we may not find it easy to arrive at an optimal decision. This is where data visualization comes in.
Data visualization refers to the graphical representation of the data. It makes large amounts of information easier to understand and helps identify patterns and trends. People can easily comprehend information and make conclusions through data visualization.
“Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space,” wrote American statistician Edward R. Tuffe, author of the book “The Visual Display of Quantitative Information.”
Understanding how to approach data visualization allows people to equip themselves with the right tools, approach, and strategies as they gather data and present them visually. This is important to businesses who want to understand consumer behavior patterns or governments seeking data-backed insights on a crisis.
Data visualization may be considered a science because it is a process and represents data methodically and accurately. Data visualization begins with volumes of information, undergoes an intensive cleaning, classification, statistical and mathematical modeling, analysis, and design process, and ends with a visualization.
On the other hand, many argue that data visualization is a language because it uses diagrams to convey meaning. Data is encoded into symbology and semiology. The syntax and conventions of these diagrams are not inherent and must be learned.
Data visualization helps to communicate analytics results in pictures. In simple words, data visualization is the language of images. That is on par with the language of words both written and spoken and with the language of numbers and statistics.
Merging science and language
Science and language do not have to invalidate each other. Their elements can go hand in hand in data visualization.
In data visualization, the challenge is how to make more people take interest in scientifically processed data. Presenting appropriate and relevant information in an engaging format through design is what makes data visualization successful. Science processes and provides information based on certain objectives while design is a form of communication shaped by visual elements.
Combined, scientific data and design can generate meaning out of raw data. The end result of data visualization is almost always a story. In storytelling, the plot (design) won’t be able to progress without the characters (scientific data) and vice versa.
Ensuring that graphs and charts present meaningful results is important now more than ever. In MicroStrategy’s “2018 Global State of Enterprise Analytics,” 63% of data-driven organizations said that implementing analytics initiatives led to high efficiency and productivity while 57% said they became more effective in decision making.
With this, the challenge for organizations is to know how to structure, format, and present their graphical data that will allow them to make faster business decisions. Sign up for The KPI Institute’s Certified Data Visualization Professional course to learn the fundamentals of creating visual representations, the most effective layouts, channel selection, and reporting best practices.
High quality data can play a huge role in increasing efficiency and improving performance and can help managers in the decision-making process. Sometimes, it is acceptable to make decisions based on instincts and gut-feelings, but the majority of them should be backed up by numbers and facts.
Data-driven decision-making is a process of collecting measurable data, based on organizational goals, extracting, and formatting data, analyzing the insights extracted from it, and using them to develop new initiatives. Nowadays, advanced software is available to help with data gathering, processing, reporting, and visualizing, to support managers.
The main steps of the decision-making process
The first step to build a well-functioning, data-driven decision-making process is to clearly define organizational goals, and to identify the questions to which the answers we find can help reach these goals. For example, if our company’s revenue goal is to increase its portion of the market share by 20% until the end of the year, a good question would be: what are the most important factors which have influence on market share?
The next step is to identify data sources and to find custodians. The source of the data highly depends on its type. There are qualitative data, which cannot be expressed by numbers, and quantitative data, which can be measured by numbers. We can collect data from primary and secondary sources. Primary sources can be observations, interviews and surveys, whilst secondary data can be collected from external documents, third-party surveys and reports.
The third main step is to clean the gathered data. During the data cleaning process, raw data is prepared for analysis by correcting incorrect, irrelevant or incomplete data. There are six data quality dimensions which should be kept in mind, during this process: Accuracy (indicates the extent to which data reflects the real world object), Completeness (refers to whether all available data is present), Consistency (refers to providing the same data, for the same object, even if this data appears in different reports), Conformity (consists in ensuring that data follows a standard format, such as YYYY/MM/DD), Timeliness (indicates whether the data was submitted in due time, respecting the data gathering deadline) and Uniqueness (points out that there should be no data duplicates reported).
Only now, the data analysis process can start. Statistical models should be used to test data and find answers to the business questions identified beforehand. Descriptive statistics can help to quantitatively describe and summarize features of data and to describe, show or summarize data in a meaningful way. For example, monthly sales or changes in employee competency levels can easily be presented in a visual manner.
Interferential statistics can help find correlations between different variables and predict future outcomes. For example, by using regression analysis, we can make a prediction on how growth, experienced in the employee competency level, can positively affect the sales volume.
Even if the data gathered is cleaned and correct, and the data analysis process has respected all the recommendations above, if the data is not presented in a meaningful way, it will not be of much use. Well-presented information and the outcomes of the analysis can help in interpreting data, thus supporting the decision-making process.
From time to time, data should be updated and re-evaluated, to make the best decisions in today’s continuously changing business environment.
Conclusions
The advanced analysis techniques and software, which are available nowadays for the majority of organizations, make it possible to build up a data-driven decision-making culture, which leads to more prudent business decisions. These tools generate more thoughtful decisions that help performance improvement, which ultimately lead to organizational growth.
