You’ve probably heard tech buzzwords like “data-driven decision making”, “advanced analytics”, “artificial intelligence (AI)”, and so on. The similarity between those terms is that they all require data. There is a famous quote in the computer science field — “garbage in, garbage out” — and it is a wonderful example of how poor data leads to bad results, which leads to terrible insight and disastrous judgments. Now, what good is advanced technology if we can’t put it to use?
The problem is clear: organizations need to have a good data management system in place to ensure they have relevant and reliable data. Data management is defined as “the process of collecting, storing, and utilizing data in a safe, efficient, and cost-effective manner”. If the scale of your organization is large, it is very reasonable to employ a holistic platform such as an Enterprise Resource Planning (ERP) system.
On the other hand, if your organization is still in its mid to early stages, it is likely that you cannot afford to employ ERP yet. However, this does not mean that your organization does not need data management. Data management with limited resources is still possible as long as the essential notion of effective data management is implemented.
Here are the four fundamental tips to start data management:
Develop a clear data storage system
Data collection, storage, and retrieval are the fundamental components of a data storage system. You can start small by developing a simple data storage system. Use cloud-based file storage, for example, to begin centralizing your data. Organize the data by naming folders and files in a systematic manner; this will allow you to access your data more easily whenever you need it.
Protect data security and set access control
Data is one of the most valuable assets in any organization. Choose a safe, reliable, and trustworthy location (if physical) or service provider (if cloud-based). Make sure that only the individuals you approve have access to your data. This may be accomplished by adjusting file permissions and separating user access rights.
Schedule a routine data backup procedure
Although this procedure is essential, many businesses still fail to back up their data on a regular basis. By doing regular backups, you can protect your organization against unwanted circumstances such as disasters, outages, and so forth. Make sure that your backup location is independent of your primary data storage location. It could be a different service provider or location, as long as the new backup storage is also secure.
Understand your data and make it simple
First, you must identify what data your organization requires to meet its objectives. The specifications may then be derived from the objectives. For example, if you are aiming to develop an employee retention program, then you will need data on employee turnover to make your data more focused and organized. Remove any data that is irrelevant to the objectives of your organization, including redundant or duplicate data.
Data management has become a necessity in today’s data-driven era. No matter what size and type of your organization, you should start doing it now. Good data management is still achievable, even with limited resources. The tips presented are useful only as a starting point for your data management journey.
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.
Nowadays, organizations need to learn more than ever to confront difficult situations, such as the COVID-19 pandemic and economic risks. Thus, they need to learn how to quickly adapt to the unpredictable in order to remain competitive.
Continuous improvement is based on learning and transferring knowledge to modify behaviors and achieve great results.
This concept of “learning organizations” was introduced in the 90s. Peter Senge, author of The Fifth Discipline: The Art & Practice of The Learning Organization, described learning organizations as “organizations that encourage adaptive and generative learning, encouraging their employees to think outside the box and work in conjunction with other employees to find the best answer to any problem”.
In this context, “Lessons Learned” is an important tool for learning organizations. It consists of knowledge obtained during a project and should be considered in future actions to improve performance.
This knowledge should be stored in a database, such as Lessons Learned Register or via wiki.
Using this tool, the project manager benefits from a great opportunity to learn from the experience of others and help them improve the profitability of the business.
Lessons Learned reflects both the positive and negative experiences of a project and can be categorized as:
Informational (e.g., how employees’ duties could change during times of emergencies)
Successful (e.g., capture effective responses to a crisis)
Problem (e.g., describe examples of actions that failed and potential ways to resolve them).
Capturing Lessons Learned should be a continuous effort throughout the life of any project and should be initiated from the beginning of the project.
Capture: It refers to bringing together information or knowledge from different sources that could be valuable for future projects. Lessons learned can be captured through text, audio, video, or image.
Store: It implies defining and deciding on the environment where Lessons Learned will be stored.
Verify: It consists of validating Lessons Learned for correctness, consistency, redundancy, and relevancy.
Distribute or Disseminate: It means spreading the knowledge in the Lessons Learned to a team, department, or organization.
Apply or Reuse: It refers to making the Lessons Learned useful to current and further projects.
Withdraw: It means recognizing when a Lesson Learned is no longer useful to current and further projects.
One option to identify Lessons Learned, is to organize Lessons Learned Sessions with the project team. During these sessions, the team members will be asked to respond to a survey which includes questions related to activities that go well, activities that do not go according to the plan, and recommended improvements.
Lessons Learned are documented in the Lessons Learned Register, which is intended to assist an organization in identifying better opportunities for improving their management practices and promote the Lessons Learned and evidence of better practices observed from a project.
Some important fields that should be included in the Lessons Learned Register are:
Category
Description of the situation
Problem/Success
Impact
Action Taken
Recommendation
The Lessons Learned Register may also include other fields considered relevant by each organization.
The knowledge gained and recorded in the Lessons Learned Register should be shared and used by project managers, team members, and leadership to decide on further projects’ activities.
Once the Lessons Learned are identified and documented, the organization should release the necessary resources to apply them. These can also include a change in culture.
Thus, organizations should strive to build a culture that recognizes when things go right and when things don’t go as planned. They can benefit from each experience and improve performance by using Lessons Learned.
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.
