How to Thread Data Literacy Into the Fabric of Your Business Culture
Organisational culture is a topic that really started to take shape and form in the early nineteen-eighties; it was around this time frame that executives started taking notice of corporate culture and the impact that it has on the organisation’s overall performance. If it is the case that the organisation is not performing, what cultural elements are impacting the negative performance and how do we change this? There is a lot of literature from an HR perspective in tackling some of the issues that arise from poorly performing organisations and the need to make a cultural shift in order to get the performance levels back on track. Five identified ways that executives can effect cultural change could be as follows: Model Behaviors: Leaders must walk the talk and lead by example. Establish a Purpose to Believe In. Set Expectations and Help People Build the Required Skills. Reinforce a Culture of Accountability. Make it Personal for Your Team. As organisations move forward into the fourth industrial revolution, and the impact of ever-increasing data volumes into businesses takes effect, are you considering the impact on your business culture for those individuals who have previously never been exposed to data, data solutions, Business Intelligence technologies and other systems that leverage data in their day-to-day jobs? Let’s take a look at point number 3 above, and in most reviews and recommended steps that executives can take, they all include or refer to building out the required skills. If we can accept that the fourth industrial revolution is real, is impacting our businesses today through the initiation of digital transformation programs and projects, underlying these initiatives is data. The question must be asked if your employees are skilled in understanding the language of data. Do they understand how data is ingested into the business, how it is transformed and processed, and how that processed data, now information, ended up in a dashboard presented to them on their screens? Do they have the skills to read, work with, investigate and communicate with that data effectively so as to contribute to the overall successful performance of the organisation? Model Behaviors. The impact that increasing data volumes and complexities will have on the organisations that we work in will be, if not already, significant. The higher up the corporate ladder one gets, the higher the value of the decisions are that get made. Executives are not immune to a lack of exposure in data literacy skills. In a recent survey, it was found that up to 32% of executives across a sample of 7,300 individuals believed that they had the required data literacy skills in place to perform in their job function. So, if executive leadership needs to walk the talk, starting at the top is a great need for leadership to increase their data literacy skills. In so doing, and showing by example how the increase in these skill levels can make a positive impact in the decisions they make, how much more effective then would the general increase in data literacy skills of all employees impact the business overall? Establish a Purpose to Believe In. Digital transformation brings to the table the prospect of job creation for jobs that are still yet to be defined. Your purpose today may soon be something of the past, as new systems and technologies begin to lay a path of direction of the business never previously considered. Think of Netflix, that started their business in 1997 in the early days of the internet. The business was about online ordering and physical delivery of your favorite movie on DVD to your house. Did they have the vision back then of what the company has become today? Their purpose was to provide their customers their favorite movies, and that still remains today, however with the advancement of systems and technologies they found a way to supply a global market that was just waiting to be serviced. What is your purpose and who are the clients you are servicing and how can the data in your business provide you insights into new ways or improvement of service delivery, or perhaps discovering new potential markets to sell to? Set Expectations and Help People Build the Required Skills. In many cases, we have found subject matter experts who are highly academically qualified who have not been exposed to data, yet their job function is placing more and more demand on them to interact with data on a daily basis. This finding has demonstrated to us that traditional academic learning paths have not included data literacy skills in the curriculum. Setting the right expectations today must include the knowledge of how data is impacting our world and making provision for upskilling in the language of data for those employees who are lagging behind. Building out the required data literacy skills can be done in one of two ways. You can build out your own internal data literacy programs, which is time-consuming, and a full-time job and the content needs constant attention and updating, or you can outsource this training requirement to a professional body that focuses on this. Reinforce a Culture of Accountability. How can we hold each other accountable for the work and the performance levels required in our organisations? By knowing that the person you rely on to deliver has the appropriate skill levels in place is a good starting point. However, the fact that that individual has a university degree does not guarantee they have the knowledge and experience when working with data. A large bank once confirmed that for a period of three years they were making regular decisions based on certain calculations of data presented to them, only to realise at a point that the calculations were inherently flawed. The executive stated that they were happy on the consistency of the decision, despite the wrong calculations, rectified the calculations and moved forward. They were happy with the accountability of the decision makers based on the information they had, recognized the negative impact on the business from prior decisions and moved forward collectively as a team toward better performance. What if this team had the required data literacy skills in place to recognize the flawed calculations earlier and avoid the bad decisions? What would the impact have been to overall performance over time? Make it Personal for Your Team. Assessing current data literacy skill levels is not about having a stick in your hand ready to beat someone up because they are lacking in their skill set. It’s about understanding each individual’s level of proficiency and then placing them on a path to improve their own personal skills, with the understanding of the potential impact that individual’s contribution can have on overall performance through the process of upskilling. Conclusion. Here we have explored but a few mechanisms for culture change. There are many, but the acknowledgement of the impact of ever-increasing data volumes into our businesses and organisations culture will help you identify which paths you will need to take. The introduction of Data Literacy skills development will assist your organisation’s ability to leverage the current data assets at your disposal, ensure high levels of success in your digital transformation programs, and equip your people with the skills required to tackle the jobs of the future driven by the digital transformation process. Let’s make you a data-driven business. By Greg Morley – Chief Data Evangelist, Expeditus (Pty)Ltd
How to Thread Data Literacy Into the Fabric of Your Business Culture
Organisational culture is a topic that really started to take shape and form in the early nineteen-eighties; it was around this time frame that executives started taking notice of corporate culture and the impact that it has on the organisation’s overall performance. If it is the case that the organisation is not performing, what cultural elements are impacting the negative performance and how do we change this? There is a lot of literature from an HR perspective in tackling some of the issues that arise from poorly performing organisations and the need to make a cultural shift in order to get the performance levels back on track. Five identified ways that executives can effect cultural change could be as follows: Model Behaviors: Leaders must walk the talk and lead by example. Establish a Purpose to Believe In. Set Expectations and Help People Build the Required Skills. Reinforce a Culture of Accountability. Make it Personal for Your Team. As organisations move forward into the fourth industrial revolution, and the impact of ever-increasing data volumes into businesses takes effect, are you considering the impact on your business culture for those individuals who have previously never been exposed to data, data solutions, Business Intelligence technologies and other systems that leverage data in their day-to-day jobs? Let’s take a look at point number 3 above, and in most reviews and recommended steps that executives can take, they all include or refer to building out the required skills. If we can accept that the fourth industrial revolution is real, is impacting our businesses today through the initiation of digital transformation programs and projects, underlying these initiatives is data. The question must be asked if your employees are skilled in understanding the language of data. Do they understand how data is ingested into the business, how it is transformed and processed, and how that processed data, now information, ended up in a dashboard presented to them on their screens? Do they have the skills to read, work with, investigate and communicate with that data effectively so as to contribute to the overall successful performance of the organisation? Model Behaviors. The impact that increasing data volumes and complexities will have on the organisations that we work in will be, if not already, significant. The higher up the corporate ladder one gets, the higher the value of the decisions are that get made. Executives are not immune to a lack of exposure in data literacy skills. In a recent survey, it was found that up to 32% of executives across a sample of 7,300 individuals believed that they had the required data literacy skills in place to perform in their job function. So, if executive leadership needs to walk the talk, starting at the top is a great need for leadership to increase their data literacy skills. In so doing, and showing by example how the increase in these skill levels can make a positive impact in the decisions they make, how much more effective then would the general increase in data literacy skills of all employees impact the business overall? Establish a Purpose to Believe In. Digital transformation brings to the table the prospect of job creation for jobs that are still yet to be defined. Your purpose today may soon be something of the past, as new systems and technologies begin to lay a path of direction of the business never previously considered. Think of Netflix, that started their business in 1997 in the early days of the internet. The business was about online ordering and physical delivery of your favorite movie on DVD to your house. Did they have the vision back then of what the company has become today? Their purpose was to provide their customers their favorite movies, and that still remains today, however with the advancement of systems and technologies they found a way to supply a global market that was just waiting to be serviced. What is your purpose and who are the clients you are servicing and how can the data in your business provide you insights into new ways or improvement of service delivery, or perhaps discovering new potential markets to sell to? Set Expectations and Help People Build the Required Skills. In many cases, we have found subject matter experts who are highly academically qualified who have not been exposed to data, yet their job function is placing more and more demand on them to interact with data on a daily basis. This finding has demonstrated to us that traditional academic learning paths have not included data literacy skills in the curriculum. Setting the right expectations today must include the knowledge of how data is impacting our world and making provision for upskilling in the language of data for those employees who are lagging behind. Building out the required data literacy skills can be done in one of two ways. You can build out your own internal data literacy programs, which is time-consuming, and a full-time job and the content needs constant attention and updating, or you can outsource this training requirement to a professional body that focuses on this. Reinforce a Culture of Accountability. How can we hold each other accountable for the work and the performance levels required in our organisations? By knowing that the person you rely on to deliver has the appropriate skill levels in place is a good starting point. However, the fact that that individual has a university degree does not guarantee they have the knowledge and experience when working with data. A large bank once confirmed that for a period of three years they were making regular decisions based on certain calculations of data presented to them, only to realise at a point that the calculations were inherently flawed. The executive stated that they were happy on the consistency of the decision, despite the wrong calculations, rectified the calculations and moved forward. They were happy with the accountability of the decision makers based on the information they had, recognized the negative impact on the business from prior decisions and moved forward collectively as a team toward better performance. What if this team had the required data literacy skills in place to recognize the flawed calculations earlier and avoid the bad decisions? What would the impact have been to overall performance over time? Make it Personal for Your Team. Assessing current data literacy skill levels is not about having a stick in your hand ready to beat someone up because they are lacking in their skill set. It’s about understanding each individual’s level of proficiency and then placing them on a path to improve their own personal skills, with the understanding of the potential impact that individual’s contribution can have on overall performance through the process of upskilling. Conclusion. Here we have explored but a few mechanisms for culture change. There are many, but the acknowledgement of the impact of ever-increasing data volumes into our businesses and organisations culture will help you identify which paths you will need to take. The introduction of Data Literacy skills development will assist your organisation’s ability to leverage the current data assets at your disposal, ensure high levels of success in your digital transformation programs, and equip your people with the skills required to tackle the jobs of the future driven by the digital transformation process. Let’s make you a data-driven business. By Greg Morley – Chief Data Evangelist, Expeditus (Pty)Ltd
A Pervasive Data Culture Helps Organisations Optimise Their Data Value Chain
In today’s data-driven world organisations need to treat data as a strategic asset Insight Consulting has worked with a large number and variety of organisations for more than a decade, and our experience has shown that successful businesses treat data as a strategic asset, underpinned by a strong data culture which grows, mines, uses and closely manages this asset. In the year 2023 and beyond, any business that does not appreciate this is not only missing out on a golden opportunity to leapfrog their competition, but also runs the risk of being left behind, as more forward-looking competitors begin to place greater emphasis on the importance of managing data across the data value chain. Data integrates into every dimension of a business’s operations and its employees, meaning that it is fundamental to corporate thinking and objectives. A strong data culture democratises data access, calibrates quality decision making faster, breeds collaboration, and fosters data literacy, all the while maintaining the right balance of control – which is fostered through robust data governance. The challenging task of creating a compelling data culture to manage data as a strategic asset requires a deliberate approach, which needs to take all stakeholders into account, and must be driven by a deep understanding of each stakeholder’s needs and touch points in the data value chain. Understanding that data culture – which includes how data is captured, seen, used and managed – underpins the efficiency of an organisation’s data value chain, how should a business go about implementing a data culture with the goal of optimising its data value chain? Crucial to the successful development of a strong data culture is sponsorship by C-suite executives and support from senior leadership. There must be a top-down approach where it is designed by and for the business users and enabled through data teams and IT. The initiative should be championed by a committee made up of business and IT people, who will be responsible for continuously driving the data culture within the organisation. The first step to creating a pervasive data culture is to assess a business’s current data maturity level. This requires organisations to accurately map out their data inventory and understand their current state in totality. This in turn means describing the current architecture and tools, mapping data inputs, outputs, interfaces and flows and the current level of data and user skills within the organisation, and identifying the strengths and weaknesses of their current state. The next step is to determine the To-Be state – what are the business objectives, and how do they see their data architecture supporting this in the ideal world. Amongst some of the many questions to be asked are: what data is required, where does it reside, what integrations are required, how will the data be used, will it be commercialised or for internal use only, will there be machine learning and AI applications, what skills are required to support the data architecture, what level of upskilling needs to be done with user community in order to allow them to use the data effectively, Whilst the temptation is to look to technology at this early stage, businesses first need to conduct a gap analysis between their as-is state and their to-be scenario before considering technology recommendations to close that gap. The gap analysis in effect sets out the requirements for the implementation of the data culture – defining, amongst other things, the desired technology features, data governance procedures, and organisational data literacy initiatives. Once the requirements are defined, the business has a roadmap for their data journey, which will allow them to break down each gap into manageable tasks encompassing technology selection and implementation, data project prioritisations, and business and technical upskilling. Ultimately, implementing a comprehensive and pervasive data culture will allow businesses to stay ahead of their competition, mitigate risk and drive the bottom line through enhanced and accurate decision making and operational efficiency. At Insight Consulting, our data professionals have a proven track record collaborating with a multitude of organisations throughout their data journeys. We take pride in our ability to listen to our customers, understand their unique strengths and challenges, and use best practice frameworks to ensure that we deliver collaborative data culture strategies that are best suited to our customers’ needs. Embrace data as your strategic asset. Contact Insight Consulting for more information on how to build a pervasive data culture to support all aspects of your data value chain.
A Pervasive Data Culture Helps Organisations Optimise Their Data Value Chain
In today’s data-driven world organisations need to treat data as a strategic asset Insight Consulting has worked with a large number and variety of organisations for more than a decade, and our experience has shown that successful businesses treat data as a strategic asset, underpinned by a strong data culture which grows, mines, uses and closely manages this asset. In the year 2023 and beyond, any business that does not appreciate this is not only missing out on a golden opportunity to leapfrog their competition, but also runs the risk of being left behind, as more forward-looking competitors begin to place greater emphasis on the importance of managing data across the data value chain. Data integrates into every dimension of a business’s operations and its employees, meaning that it is fundamental to corporate thinking and objectives. A strong data culture democratises data access, calibrates quality decision making faster, breeds collaboration, and fosters data literacy, all the while maintaining the right balance of control – which is fostered through robust data governance. The challenging task of creating a compelling data culture to manage data as a strategic asset requires a deliberate approach, which needs to take all stakeholders into account, and must be driven by a deep understanding of each stakeholder’s needs and touch points in the data value chain. Understanding that data culture – which includes how data is captured, seen, used and managed – underpins the efficiency of an organisation’s data value chain, how should a business go about implementing a data culture with the goal of optimising its data value chain? Crucial to the successful development of a strong data culture is sponsorship by C-suite executives and support from senior leadership. There must be a top-down approach where it is designed by and for the business users and enabled through data teams and IT. The initiative should be championed by a committee made up of business and IT people, who will be responsible for continuously driving the data culture within the organisation. The first step to creating a pervasive data culture is to assess a business’s current data maturity level. This requires organisations to accurately map out their data inventory and understand their current state in totality. This in turn means describing the current architecture and tools, mapping data inputs, outputs, interfaces and flows and the current level of data and user skills within the organisation, and identifying the strengths and weaknesses of their current state. The next step is to determine the To-Be state – what are the business objectives, and how do they see their data architecture supporting this in the ideal world. Amongst some of the many questions to be asked are: what data is required, where does it reside, what integrations are required, how will the data be used, will it be commercialised or for internal use only, will there be machine learning and AI applications, what skills are required to support the data architecture, what level of upskilling needs to be done with user community in order to allow them to use the data effectively, Whilst the temptation is to look to technology at this early stage, businesses first need to conduct a gap analysis between their as-is state and their to-be scenario before considering technology recommendations to close that gap. The gap analysis in effect sets out the requirements for the implementation of the data culture – defining, amongst other things, the desired technology features, data governance procedures, and organisational data literacy initiatives. Once the requirements are defined, the business has a roadmap for their data journey, which will allow them to break down each gap into manageable tasks encompassing technology selection and implementation, data project prioritisations, and business and technical upskilling. Ultimately, implementing a comprehensive and pervasive data culture will allow businesses to stay ahead of their competition, mitigate risk and drive the bottom line through enhanced and accurate decision making and operational efficiency. At Insight Consulting, our data professionals have a proven track record collaborating with a multitude of organisations throughout their data journeys. We take pride in our ability to listen to our customers, understand their unique strengths and challenges, and use best practice frameworks to ensure that we deliver collaborative data culture strategies that are best suited to our customers’ needs. Embrace data as your strategic asset. Contact Insight Consulting for more information on how to build a pervasive data culture to support all aspects of your data value chain.
Getting to Grips With Data
As shared in a recent article on ITWeb (https://www.itweb.co.za/content/lLn147mQjKa7J6Aa ) The success or failure of a data project relies on the organisation’s ability to move from raw data to an analytics-ready state. In this second article on data literacy and its importance, I will look at the data lifecycle and then how to integrate the right levels of data literacy into your organisation. Previously, I discussed why data is so important in business and thus why it is so imperative that every employee can ‘speak data’. Before we get to the question of how to make the organisation data literate, we need firstly to recognise that data itself has a distinct life cycle, which needs to be carefully managed. The value of data is only unlocked once an improved decision is made due to the use of the data and that decision brings an improved action. But before we can get to insight, we need analytics, and before we can get to analytics, we need analytics-ready data, and before we can get there, we need raw data. The process to go from raw data to insight and action is called the data value chain and there are different people involved in that process. Data comes from a variety of sources, among them internal systems (such as the enterprise resource planning, customer relationship management and HR applications) as well as external sources. The latter could include the internet of things, social media and so on. All of this data will come in a multiplicity of forms, structured and unstructured. Raw data needs to be managed and integrated to make it available for analytics. The value of data is only unlocked once an improved decision is made due to the use of the data and that decision brings an improved action. Raw data is consolidated into data lakes where it can be categorised; from there, it can be transferred into the data warehouse and marts. The data then must be integrated and then analysed. The final step is to provide users with various tools to help them discover what data is present, and then request reports or create dashboards relating to data that will be useful to them. Each one of these steps must be undertaken by people with the requisite skills. The kinds of job titles we are looking at include data scientist, ETL (extract, transform, load) specialists, data architects, database administrators, data engineers, business intelligence professionals and data scientists. However, please do not lose sight of the important point made in the first article: ultimately, the end-users of the data insights generated will be the business decision-makers and operational staff who would typically be the least data literate. In other words, the success or failure of the whole data project relies on the organisation’s ability to move from raw data to an analytics-ready state and for the business staff to translate that analytics data into action-ready insight. Obstacles to creating a data-literate organisation Before considering how to improve the organisation’s data literacy, one needs to be aware of common barriers. These are: Resistance from the workforce, including the C-suite. Change is always hard, and most employees are used to relying on some element of gut feel to make decisions. The case for basing decisions on evidence needs to be carefully made. This effort must span the whole organisation − Qlik research showed that only 32% of the C-suite is data literate. Data champions need to be identified across the organisation, and the chief data officer or chief analytics officer needs to play the champion’s role at the top. The need for governance. As more and more data becomes available, employees are starting to use a growing number of datasets to help them make better decisions. This democratisation of data has the effect of driving decision-making further down the organisational hierarchy but, like the similar problem of shadow IT, it can end up creating a chaotic environment. The organisation’s leadership must provide governance along with self-service capability for business users. At a practical level this could be achieved through using a uniform platform on which to deploy new datasets and applications. Overcome employee insecurity. Increasing use of data can be seen as a threat to job security, in the same way that automation and robotics are. Most employees, even young ones, are also not certain they are data-proficient. Company training in this important area is crucial. Break down organisational silos. Organisations must take care to ensure the data-literate or specialist employees they employ are not isolated in IT or business intelligence teams. One approach is to establish forums in which data leaders or champions can share knowledge and answer questions from the entire employee body. How to create a data-literate organisation The process for changing the organisational culture to one in which data plays a central role involves four basic steps: Communicate the power of data. This process needs to take place across the whole organisation and should be consistent. Providing practical examples is recommended. For example, showcase how a customer insight led to a new business opportunity, or how an individual was able to gain approval of a new idea by backing it up with facts and figures. It would also help if leadership reported regularly on how the use of data improved important metrics. Assess progress. Organisations need to begin by understanding the current status of their workforce’s data literacy, and then track progress. A granular understanding of how each part of the organisation is progressing on the journey towards data literacy will also help in customising training programmes. Establish training programmes for each type of data user. As noted in the first article, there are four main kinds of data user. Training needs to be appropriate to how an individual will use data. Qlik research showed 66% of respondents believe they have received adequate data training − one-third of employees need to be upskilled. Repeat. As already noted, the volume of data currently being generated is unprecedented. The skills to deal with this volume of material, as well as the emergence of new types of data, need to be refreshed constantly − data literacy is truly an ongoing journey. Here’s a top tip: assess new joiners; if skills are lacking − get them onto a foundation programme immediately as part of the onboarding process. Creating a data-literate workforce can seem like a mammoth task but, as the old adage goes, “the only way to eat an elephant is one bite at a time”. As I have argued, data literacy is essential to the sustainability of all organisations, so it is vital this process begins at once. ABOUT THE AUTHOR Kevin van der Merwe Sales director iOCO Qlik.
Getting to Grips With Data
As shared in a recent article on ITWeb (https://www.itweb.co.za/content/lLn147mQjKa7J6Aa ) The success or failure of a data project relies on the organisation’s ability to move from raw data to an analytics-ready state. In this second article on data literacy and its importance, I will look at the data lifecycle and then how to integrate the right levels of data literacy into your organisation. Previously, I discussed why data is so important in business and thus why it is so imperative that every employee can ‘speak data’. Before we get to the question of how to make the organisation data literate, we need firstly to recognise that data itself has a distinct life cycle, which needs to be carefully managed. The value of data is only unlocked once an improved decision is made due to the use of the data and that decision brings an improved action. But before we can get to insight, we need analytics, and before we can get to analytics, we need analytics-ready data, and before we can get there, we need raw data. The process to go from raw data to insight and action is called the data value chain and there are different people involved in that process. Data comes from a variety of sources, among them internal systems (such as the enterprise resource planning, customer relationship management and HR applications) as well as external sources. The latter could include the internet of things, social media and so on. All of this data will come in a multiplicity of forms, structured and unstructured. Raw data needs to be managed and integrated to make it available for analytics. The value of data is only unlocked once an improved decision is made due to the use of the data and that decision brings an improved action. Raw data is consolidated into data lakes where it can be categorised; from there, it can be transferred into the data warehouse and marts. The data then must be integrated and then analysed. The final step is to provide users with various tools to help them discover what data is present, and then request reports or create dashboards relating to data that will be useful to them. Each one of these steps must be undertaken by people with the requisite skills. The kinds of job titles we are looking at include data scientist, ETL (extract, transform, load) specialists, data architects, database administrators, data engineers, business intelligence professionals and data scientists. However, please do not lose sight of the important point made in the first article: ultimately, the end-users of the data insights generated will be the business decision-makers and operational staff who would typically be the least data literate. In other words, the success or failure of the whole data project relies on the organisation’s ability to move from raw data to an analytics-ready state and for the business staff to translate that analytics data into action-ready insight. Obstacles to creating a data-literate organisation Before considering how to improve the organisation’s data literacy, one needs to be aware of common barriers. These are: Resistance from the workforce, including the C-suite. Change is always hard, and most employees are used to relying on some element of gut feel to make decisions. The case for basing decisions on evidence needs to be carefully made. This effort must span the whole organisation − Qlik research showed that only 32% of the C-suite is data literate. Data champions need to be identified across the organisation, and the chief data officer or chief analytics officer needs to play the champion’s role at the top. The need for governance. As more and more data becomes available, employees are starting to use a growing number of datasets to help them make better decisions. This democratisation of data has the effect of driving decision-making further down the organisational hierarchy but, like the similar problem of shadow IT, it can end up creating a chaotic environment. The organisation’s leadership must provide governance along with self-service capability for business users. At a practical level this could be achieved through using a uniform platform on which to deploy new datasets and applications. Overcome employee insecurity. Increasing use of data can be seen as a threat to job security, in the same way that automation and robotics are. Most employees, even young ones, are also not certain they are data-proficient. Company training in this important area is crucial. Break down organisational silos. Organisations must take care to ensure the data-literate or specialist employees they employ are not isolated in IT or business intelligence teams. One approach is to establish forums in which data leaders or champions can share knowledge and answer questions from the entire employee body. How to create a data-literate organisation The process for changing the organisational culture to one in which data plays a central role involves four basic steps: Communicate the power of data. This process needs to take place across the whole organisation and should be consistent. Providing practical examples is recommended. For example, showcase how a customer insight led to a new business opportunity, or how an individual was able to gain approval of a new idea by backing it up with facts and figures. It would also help if leadership reported regularly on how the use of data improved important metrics. Assess progress. Organisations need to begin by understanding the current status of their workforce’s data literacy, and then track progress. A granular understanding of how each part of the organisation is progressing on the journey towards data literacy will also help in customising training programmes. Establish training programmes for each type of data user. As noted in the first article, there are four main kinds of data user. Training needs to be appropriate to how an individual will use data. Qlik research showed 66% of respondents believe they have received adequate data training − one-third of employees need to be upskilled. Repeat. As already noted, the volume of data currently being generated is unprecedented. The skills to deal with this volume of material, as well as the emergence of new types of data, need to be refreshed constantly − data literacy is truly an ongoing journey. Here’s a top tip: assess new joiners; if skills are lacking − get them onto a foundation programme immediately as part of the onboarding process. Creating a data-literate workforce can seem like a mammoth task but, as the old adage goes, “the only way to eat an elephant is one bite at a time”. As I have argued, data literacy is essential to the sustainability of all organisations, so it is vital this process begins at once. ABOUT THE AUTHOR Kevin van der Merwe Sales director iOCO Qlik.
Give Data Purpose – Agile Data Management and AI driven Analytics
The need for organisations to have access to trusted and reliable information for sound decision making has not changed since Data Warehousing became topical in the 1980s. In fact, almost 30 years later the need is far greater. Organisations are dealing with a plethora of data from a variety of data sources, including streaming data, IOT, or even the ‘Internet of Everything’ related data and can you believe it, Flat File data as well. Oh yeah, Excel is still in the picture, fortunately or unfortunately. Historically, data management teams spent many a late-night drinking flat coke and eating stale pizza making every effort to ensure that the relevant data sources have been identified, that the data source connectors are performing and ensuring that the data is extracted, transformed, and loaded into a data management system optimised for user access through specialised Business Intelligence, Reporting and Analytical tools. Now, the need for near real-time access or even real-time access to that sourced and managed data puts the entire process at risk. Users want, no demand access to up to date data to enable them to make informed business decisions. The idea of ‘Data Batch loading windows’ need to be reconsidered considering modern data management techniques and user requirements. It is key to start with a foundation based on sound data management principles aligned to data governance practices to enable business innovation and growth, one that promises valuable market and customer insights. Yet many organizations struggle to determine where to begin with a unified data and analytics initiative. Organisations need guidance and pragmatic steps to unlock the value of enterprise data management and AI driven analytics. A framework should be considered to identify and target investment areas based on desired business outcomes and identifiable analytics patterns. Where is the best place to start? Modern data governed organisations should build a data and analytics strategy taking into consideration the enterprises data literacy maturity level of the organisation, supported by a well-structured data governance framework. One important starting point is to identify the appropriate investments needed to build on your data and analytics experience with the goal to provide self-service capabilities that can be employed with little to no IT support. Modern Data Management must be based on the ability to integrating data across the IT landscape with the aim to provide data consumers with intelligent, relevant, and contextual insights for informed and insightful decision-making augmented by Artificial Intelligence. The main idea behind modern data management is to Process distributed data. Data Architects and Data Integrators need to evolve data integration to data orchestration by using an enterprise data fabric. This enterprise data fabric for enterprise-wide data management should enable the ability to easily discover, classify, profile, understand, and prepare all your data through an enterprise data catalogue. The outcome of this is to help organisations manage, govern, integrate, and optimize enterprise data. Let the past, be a foundation for your next step. A lot of emphasis was placed on visualising data. The emphasis of traditional Business Intelligence Tools was on visualising the data. But focusing only on data visualisation and trying best to interpret the data can lead to un-insightful decision making. Making decisions without AI driven data insights can be like putting lipstick on a pig. No real outcome other than beautiful dashboards with spinning charts and fancy colours. Only focusing on the presentation of data and not the quality data has its own repercussions. Garbage in, Garbage out. A joint effort between business and IT must be established to ensure data accountability. Accountability must be in line with your enterprise data governance framework and supported by a data literate user community. With this approach joint approach, supported by investments in unified data and analytics platforms, the organisation can develop required data and analytics capabilities for managing data and driving the right organisational growth, efficiency, and financial impact. To use data-based insights augmented by AI to make fast, well-informed decisions, organisations should evaluate a unified, business-centric platform with an open architecture that turns data into business value. This unified, business centric open platform should offer compute resource flexibility, the unified data management and analytics platform must support accelerated organisational outcomes through data orchestration, data management and data-based insights augmented by AI. A platform likes this will lead to data-to-value outcomes across the organisation. Modern data-to-value platforms should be cloud based to capitalise on all the benefits of cloud. Allowing for interconnectivity to Off-Cloud (i.e., On-premises) systems in a hybrid and modular data architecture. Gone are the days of hardware provision, with a 5-year lifecycle and recuring procurements processes. When you need more compute power to support your data-to-value strategy, it should be available as and when it is needed. ABOUT THE AUTHOR Shaid FA Greeff SAP Business Technology Platform Solution Advisor (SAP Data Warehouse Cloud, Data Intelligence, Analytics Cloud) SAP
Give Data Purpose – Agile Data Management and AI driven Analytics
The need for organisations to have access to trusted and reliable information for sound decision making has not changed since Data Warehousing became topical in the 1980s. In fact, almost 30 years later the need is far greater. Organisations are dealing with a plethora of data from a variety of data sources, including streaming data, IOT, or even the ‘Internet of Everything’ related data and can you believe it, Flat File data as well. Oh yeah, Excel is still in the picture, fortunately or unfortunately. Historically, data management teams spent many a late-night drinking flat coke and eating stale pizza making every effort to ensure that the relevant data sources have been identified, that the data source connectors are performing and ensuring that the data is extracted, transformed, and loaded into a data management system optimised for user access through specialised Business Intelligence, Reporting and Analytical tools. Now, the need for near real-time access or even real-time access to that sourced and managed data puts the entire process at risk. Users want, no demand access to up to date data to enable them to make informed business decisions. The idea of ‘Data Batch loading windows’ need to be reconsidered considering modern data management techniques and user requirements. It is key to start with a foundation based on sound data management principles aligned to data governance practices to enable business innovation and growth, one that promises valuable market and customer insights. Yet many organizations struggle to determine where to begin with a unified data and analytics initiative. Organisations need guidance and pragmatic steps to unlock the value of enterprise data management and AI driven analytics. A framework should be considered to identify and target investment areas based on desired business outcomes and identifiable analytics patterns. Where is the best place to start? Modern data governed organisations should build a data and analytics strategy taking into consideration the enterprises data literacy maturity level of the organisation, supported by a well-structured data governance framework. One important starting point is to identify the appropriate investments needed to build on your data and analytics experience with the goal to provide self-service capabilities that can be employed with little to no IT support. Modern Data Management must be based on the ability to integrating data across the IT landscape with the aim to provide data consumers with intelligent, relevant, and contextual insights for informed and insightful decision-making augmented by Artificial Intelligence. The main idea behind modern data management is to Process distributed data. Data Architects and Data Integrators need to evolve data integration to data orchestration by using an enterprise data fabric. This enterprise data fabric for enterprise-wide data management should enable the ability to easily discover, classify, profile, understand, and prepare all your data through an enterprise data catalogue. The outcome of this is to help organisations manage, govern, integrate, and optimize enterprise data. Let the past, be a foundation for your next step. A lot of emphasis was placed on visualising data. The emphasis of traditional Business Intelligence Tools was on visualising the data. But focusing only on data visualisation and trying best to interpret the data can lead to un-insightful decision making. Making decisions without AI driven data insights can be like putting lipstick on a pig. No real outcome other than beautiful dashboards with spinning charts and fancy colours. Only focusing on the presentation of data and not the quality data has its own repercussions. Garbage in, Garbage out. A joint effort between business and IT must be established to ensure data accountability. Accountability must be in line with your enterprise data governance framework and supported by a data literate user community. With this approach joint approach, supported by investments in unified data and analytics platforms, the organisation can develop required data and analytics capabilities for managing data and driving the right organisational growth, efficiency, and financial impact. To use data-based insights augmented by AI to make fast, well-informed decisions, organisations should evaluate a unified, business-centric platform with an open architecture that turns data into business value. This unified, business centric open platform should offer compute resource flexibility, the unified data management and analytics platform must support accelerated organisational outcomes through data orchestration, data management and data-based insights augmented by AI. A platform likes this will lead to data-to-value outcomes across the organisation. Modern data-to-value platforms should be cloud based to capitalise on all the benefits of cloud. Allowing for interconnectivity to Off-Cloud (i.e., On-premises) systems in a hybrid and modular data architecture. Gone are the days of hardware provision, with a 5-year lifecycle and recuring procurements processes. When you need more compute power to support your data-to-value strategy, it should be available as and when it is needed. ABOUT THE AUTHOR Shaid FA Greeff SAP Business Technology Platform Solution Advisor (SAP Data Warehouse Cloud, Data Intelligence, Analytics Cloud) SAP
Most Important Elements of a Data Literacy Program
As we enter the latter half of 2022, it is a clearly stated fact that companies around the world have identified lack in data literacy skills as a prohibitive factor in the successful deployment and adoption of digital transformation programs. At the end of 2019, Gartner predicted that up to 80% of organisations would embark on implementing a data literacy program during the course of 2020. That was before the impact of the Covid-19 pandemic. Where are you at? With this is mind, developing an in-house data literacy program can be accomplished, but at what cost to your business? What are the skills required in-house in order to do just that? If you have the skills, what is the capacity of these resources to take on a project of this magnitude to ensure delivery in a timely fashion, required by the business to keep moving forward? Some organisations we have been engaging with are doing just that. They are dedicating resources to some sort of internal program. One organisation in particular has done a complete review of all the BI and Data Analytics resources to understand an As-Is situation of their business and have now started to develop an in-house program. When asking more questions about their approach, it turns out that the program they are building is focused on helping their existing professional resources to increase their own skills, with a focus to achieve a Data Scientist level type qualification. There is nothing wrong with this, as this client being in financial services has a great need for Data Science related work and will certainly gain benefit from their efforts. But, what about the rest of their business people? Those earmarked to consume all that information once generated. Where are their skills sets regarding data literacy and are they being considered in the process? The first thing to consider in your Data Literacy program is to understand where you stand to gain the most benefit from. Element Number 1: Understanding where your team, department or overall organisation stands in their current data literacy skill levels is the first step to make. You will need a solid scientific based framework to perform the assessments. The insights gained through this process are crucial for making decisions about how much budget should be allocated, and exactly where this budget needs to be spent, in what areas and for which people to help the individual move from their current base to a higher skill level, and in so doing increase the overall team, department or the organisations overall data literacy skill levels. Element Number 2: Now that you understand where your people in their respective groups stand, do you have the right content and mechanism to deliver the right training programs for each individual. What is the framework within which each individual is classified based on their assessments? And how do I select the right training content for each person individually based on their assessment results. Ensuring you have great training material and up to date content is key for the successful rollout of your program. Element Number 3: Do you have accredited training facilitators in place to help you deliver the training material? Any person embarking on a training program wants to ensure that on the successful completion of their training that they are officially recognised for their efforts, and they can take their new learnings directly back to the office and immediately show the benefits of the newly acquired skills. If this is not the case, perhaps you have Senior or Principal level skills in your Data and Analytics departments that could take on this role. What would the impact be on their current workloads, do they have capacity to take on the additional workload? Element Number 4: Now that you have delivered the data literacy training, have you considered how to keep improving the material as the market keeps moving forward. Have you considered a framework approach to threading a data literacy data centred culture into the fabric of your current organisational culture? How will you help your organisation shift its current culture to one that is most certainly being driven by digital transformation projects and the 4th Industrial revolution? Conclusion: No matter where your organisation currently stands with regard to the elements required, the bottom line is that Data Literacy skills are the base core foundational needs for an ever-increasing digital world that we all operate in. Making the investment to improve the current skills has clear return on investment outcomes that will help resolve current inefficiencies and lay a foundation for future job skill requirements that digital transformation is pushing into our everyday workplace. Let’s make you a Data Driven Business! About the Author Greg Morley Sales Director Expeditus (Pty) Ltd.
Most Important Elements of a Data Literacy Program
As we enter the latter half of 2022, it is a clearly stated fact that companies around the world have identified lack in data literacy skills as a prohibitive factor in the successful deployment and adoption of digital transformation programs. At the end of 2019, Gartner predicted that up to 80% of organisations would embark on implementing a data literacy program during the course of 2020. That was before the impact of the Covid-19 pandemic. Where are you at? With this is mind, developing an in-house data literacy program can be accomplished, but at what cost to your business? What are the skills required in-house in order to do just that? If you have the skills, what is the capacity of these resources to take on a project of this magnitude to ensure delivery in a timely fashion, required by the business to keep moving forward? Some organisations we have been engaging with are doing just that. They are dedicating resources to some sort of internal program. One organisation in particular has done a complete review of all the BI and Data Analytics resources to understand an As-Is situation of their business and have now started to develop an in-house program. When asking more questions about their approach, it turns out that the program they are building is focused on helping their existing professional resources to increase their own skills, with a focus to achieve a Data Scientist level type qualification. There is nothing wrong with this, as this client being in financial services has a great need for Data Science related work and will certainly gain benefit from their efforts. But, what about the rest of their business people? Those earmarked to consume all that information once generated. Where are their skills sets regarding data literacy and are they being considered in the process? The first thing to consider in your Data Literacy program is to understand where you stand to gain the most benefit from. Element Number 1: Understanding where your team, department or overall organisation stands in their current data literacy skill levels is the first step to make. You will need a solid scientific based framework to perform the assessments. The insights gained through this process are crucial for making decisions about how much budget should be allocated, and exactly where this budget needs to be spent, in what areas and for which people to help the individual move from their current base to a higher skill level, and in so doing increase the overall team, department or the organisations overall data literacy skill levels. Element Number 2: Now that you understand where your people in their respective groups stand, do you have the right content and mechanism to deliver the right training programs for each individual. What is the framework within which each individual is classified based on their assessments? And how do I select the right training content for each person individually based on their assessment results. Ensuring you have great training material and up to date content is key for the successful rollout of your program. Element Number 3: Do you have accredited training facilitators in place to help you deliver the training material? Any person embarking on a training program wants to ensure that on the successful completion of their training that they are officially recognised for their efforts, and they can take their new learnings directly back to the office and immediately show the benefits of the newly acquired skills. If this is not the case, perhaps you have Senior or Principal level skills in your Data and Analytics departments that could take on this role. What would the impact be on their current workloads, do they have capacity to take on the additional workload? Element Number 4: Now that you have delivered the data literacy training, have you considered how to keep improving the material as the market keeps moving forward. Have you considered a framework approach to threading a data literacy data centred culture into the fabric of your current organisational culture? How will you help your organisation shift its current culture to one that is most certainly being driven by digital transformation projects and the 4th Industrial revolution? Conclusion: No matter where your organisation currently stands with regard to the elements required, the bottom line is that Data Literacy skills are the base core foundational needs for an ever-increasing digital world that we all operate in. Making the investment to improve the current skills has clear return on investment outcomes that will help resolve current inefficiencies and lay a foundation for future job skill requirements that digital transformation is pushing into our everyday workplace. Let’s make you a Data Driven Business! About the Author Greg Morley Sales Director Expeditus (Pty) Ltd.