2021 Digest
Jose Almeida
Data Consultant/Advisor | ???? ???? ???? ???? ???? ???? ???? | Data Strategy | Data Governance | Data Quality | Master Data Management | Remote/Onsite Consulting Services in EMEA
?Managing an organization’s data assets improves their decisions, their customers experience and loyalty, it impacts offer and innovation, impacts operation efficiency, it impacts processes, minimizing inefficiencies and reducing costs, it impacts risk and compliance, and may generate new revenue streams.
?Being a data-driven organization - This is an objective that is continuously formulated over the past decade – yet continuously failed.
?Kenya’s data protection framework, the Data Protection Act (DPA) of 2019 roll-out is still in an emerging phase, and it’s important to increase the awareness of its challenges and opportunities. (In collaboration with Gideon Aswani and Pathways International ).
?A data strategy must answer the question: How will data enable the business strategy?
?Data governance may not seem to be the highest priority and it used to be a nice to have, but in a context with increasing focus and importance of data and analytics, it turned into a must have for any organization that wants to enable business value from data.
Data governance is at the heart of any data related discipline or initiative. Why? For one reason, data governance provides the foundation for all of them.
?Register Now – Data Protection Act - Roadmap to Compliance. Join me and Gideon Aswan in a webinar on the personal data protection and the road to compliance with the Data Protection Act, 2019 (DPA) where we’ll be discussing:
?????????????The case for regulation of personal data usage
?????????????Expectations of the regulator
?????????????How do you comply with the regulations?
?????????????What opportunities come with Personal Data? The webinar will be held on December 2nd 2021 at 11:00am EAT.
?Data is at the core of marketing strategies in every organization. It’s the basis for insights to better understand target audiences and what makes marketing campaigns successful.
That is, if when saying data, we’re talking about quality data. Data that is complete, consistent, accurate, timely and available. These are the necessary conditions for any marketing department to make data the foundation of their strategy.
Low-quality data produces low-quality insights. Low-quality insights produce poor business decisions.
?Data Governance won’t sell itself.
?Data governance is essential when approaching cyber security or data protection.
To protect against threats, to comply with data protection and additional regulations, it’s crucial for organisations to know what data to protect and how to protect it.
Data governance allows an organisation to identify its the most sensitive and critical data and assign the necessary resources to protect it.
Being sure that no organization can be 100% secure and not many have the resources – people and financial – to fully implement, operate, and improve the necessary measures, the approach has to be, for most of them, to take a focused, risk-based approach.
?Driving better insights through better data.
?A few notes on a Forbes article that delves on CDOs sleeping habits.
The awareness of the strategic importance of data exists.
The awareness of the positive effects of governed data exists.
Yet most organizations are still slow adopters of data governance frameworks, or non-adopters at all - risking poor strategic decision making and misallocation of critical resources.
?The purpose of data is to create business value, so the data strategy, must be oriented towards the organization's strategic priorities and key business objectives. It must be the business prerogative to determine what are the priorities and objectives, all these initiatives should be driven and oriented by the business units and grounded on clear business use cases – aligned with strategical business objectives.
Start with small, targeted initiatives, where the business impact and value can be clearly identified – Success, is dependent on business success.
?7K subscribers
There are additional challenges when trying to integrate data from multiple, disparate sources, often incompatible, with the same data being produced and handled under different rules, with different formats across the organization’s different systems.
This will often lead to increasingly complex project, that will consume time, resources and most likely lead to an underperforming solution.
Before this happens it's important to consider the scope of data to be integrated, what is in fact critical considering the objectives and necessary to generate maximum value from the solution.
?Even at the risk of having no readers for this article, I decided to boldly give it a tedious title. Besides being self-explanatory, it’s about something important that is often overlooked. To redeem myself of the lack of an imaginative title I promise to thank personally to however signals in the comments that they went all the way to the end of the article. For all others my apologies in advance.
?Trust is the first victim to fall at the hands of ungoverned data, of failed or under-performing data initiatives.
?I’ve been looking more closely into the insurance industry, especially into the East African insurance markets and there are some ideas that I believe may contribute to further discuss the critical role that data must play in the incoming challenges caused by ongoing market disruptions, reflected directly on lower premiums and higher claims.
The business landscape is changing for good. To remain competitive, insurers must act now and start or accelerate their data-driven transformation to drive enhanced digital experiences and reduce costs.
?It’s not. It shouldn’t be.
It should be contributing to better results, to better performance, to better decisions.
So, Data Quality shouldn’t be a money pit, but in some awkward way it is happening.
?What to do when a renown French anthropologist seems to be making the most famous blue jeans in the market? (when you can’t see the difference between Lévi-Strauss and Levi Strauss).
?Truly honored and proud to be included in the DataManagementU Thought Leaders list, alongside with some brilliant minds in the industry.
?In the absence of a master data management solution, the most usual approach has been to bridge these siloed systems by building point-to-point interfaces, leading to complex and expensive IT solutions that fall short on the initial objectives.
?In the usual siloed ecosystem, the most common scenario in most organizations, master data is scattered across multiple systems, governed by multiple rules, managed by multiple processes, collected, and updated under different conditions and replicated trough ad-hoc processes, unavoidably leading to lack of quality. By definition - data that lacks quality is unfit for use. Considering the master data is a critical part of most of an organization’s processes, from the factory floor to the board, it’s easy to conclude how broad the impacts of bad master data are.
?Most organizations evolved considering specialization as tool to achieve a greater degree of efficiency, there is however a downside - looking at it from the perspective of the journey to become data-driven organizations – specialization leads inevitably to compartmentalization, that will create obstacles when managing data as a corporate asset and consequently on the objectives of a data-driven transformation.
?Looking at any organization’s data landscape, we can see that these defining priorities, to design a strategy for data governance can be a challenge. One of the tools that can help determine the scope of data to be included and prioritized is a Criticality Matrix.
?Last week this newsletter hit a surprising, at least for me, milestone – 6,000 subscribers. That is quite impressing, when writing week after week about the backstage of the world of data. Knowing that there are 6,000 people wanting to read, not about the glamours world of machine learning, AI or advanced analytics, but about all the work that need to be done behind curtains to enable these powerful tools to deliver reliable, actionable insights – It’s definitely a surprise. After all these weeks writing about different aspects of data governance, data quality or master data, of enforcing the importance of trust, on how critical data is for the corporate decision processes, I’m putting it in the hands of who has given me their time and challenging myself to do my best to pick your ideas for future issues of this newsletter.
What Next?
Share your ideas on the comments or via direct message. And most importantly, thank you for your time and support.
?Approaching data governance in an adaptative, incremental and compounding way will produce long-term benefits, creating traction and increasing the awareness across the organization and will end-up acting as the motor from within the organization for a Data Governance structure that will?grow organically out of the initial iteration. There are no predefined paths, and most likely the most travelled path is not the one that produces those best results in every context.
?The awareness of the strategic importance of data exists, and still most organizations are slow adopters of data governance frameworks, risking poor strategic decision making and misallocation of critical resources.
?I entered the world of data, starting by data quality, making quality one of the foundational themes for all the work I’ve been producing since. “If I have seen further it is by standing on the shoulders of Giants” is one of my favorite quotes, belonging to Isaac Newton, working as a reminder the everything we know and do is a compound of work done before us. One of these giants is Joseph M. Juran, whose work in the field of quality management is still a reference. So why am I bringing Juran here?
?Trust is easier to destroy than to build, this leads us to what I believe are the most critical success factors for any data governance initiative, data governance is established top-down and develops bottom-up.
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?Organic, because allowing data governance to grow from within, will allow data into new dimension, walking into a more data and digitally driven approach, where data plays a new role as a business asset, creating value to the organization.
?Insurance companies focused on taking advantage of these opportunities need to develop their strategies around the creation of innovative, customer focused products, channels and services, in other words, making digital transformation a top priority.
?For a reason or another, SMEs have somehow kept out of my radar, and I’ve only recently started reflecting on this and looking closer what can be the benefits and challenges for SMEs to become data-driven and on how this process can be conducted. My thanks to Charles for driving my attention into this subject.
?Deploying master data management can sometimes provide us an experience like when our quiet hike turns into quite an adventure as we enter uncharted territory. Struggling through all the challenges in an MDM project is not for the faint-hearted. It doesn’t have to be that way.
?Although a bit off topic for the purpose of this newsletter, and for that I apologize from start, but I’m very proud to announce this new partnership and excited with the potential synergy that will come out of it, a step in transforming the East African market in the direction of a data-driven economy.
?As we watch organizations struggling to collect as much data as possible, we can also see the infrastructure, storage, processing, and analysis costs increasing at the same time as the quality of analysis and insights is decreasing.
?These numbers don’t show that this is not the reality for most organizations that started this journey. It is important to be aware that for most organizations the results of digitalization processes fall short from the objectives and will settle for dilution of value and mediocre performance, confronted with a situation where they simply assume that the investment was wasted and worse than that, accept to live with mediocre, under-performing solutions – expensive failures.
?Organizations face massive challenges to enact the changes needed to make the digital leap and to recognize the huge amount of impact digital will have, and the scale of the effort required to drive those changes.
?Investing in data capabilities and technology without clearly defined business purpose and objectives, is a step towards failure. Data’s purpose is to create value, so any data strategy must be oriented towards the organization's strategic priorities and key business objectives.
?FIs that can implement robust data governance frameworks – focusing on having the best quality data feeding the decision processes - will be able to succeed and extract maximum value from their data, and those that do not will fail.
?I'll be on Data Ki Baat? next Saturday, talking about why it's not about data.
?Data governance may not seem to be the highest priority. But this is the time to address issues that have always existed in all types of organizations.
?Going through the African Financial Industry Barometer, released by Africa CEO Forum and Deloitte Africa, and reading it from a Data Management perspective there are a few interesting takes that I would like to share.
?I’m often asked about use cases for data governance – I always have the same answer, there are none, there are no use cases for data governance.
There are business cases.
?At first sight, pushing Bruce Lee into an article about data might seem a bit of a stretch, I don’t think so, I believe it does make sense, so bear with me a few minutes.
?Can we think of data silos as an agile framework to achieve business objectives? Can data silos be used as part of a solid data foundation within the organization?
?? Forget data strategy
? Forget use cases
? Forget data
?? Deliver business value
?What kind of company does not know who their customers or suppliers are?
?Preparing an immense, global, corporate level Data Governance Program is comfortable.
?? It’s comfortable because nothing is taking place.
?? It’s comfortable because there’s no commitment.
?? It’s comfortable because there’s no results.
?From Charles Duhigg’s The Power of Habit we can draw some parallels to a frequent problem when dealing with Data Quality issues, and what we can learn from the science of habit formation.
?Being a data scientist may be considered as the sexiest job within the data related jobs, but it has its challenges, especially when it comes to demonstrate the value created by their work. In this article, let us look at some of those challenges, and how they can be overcome when organizations take on a systematic approach on how to manage their data.
?I am the first one to agree that a strategic approach to data quality, should be the way to go. Data Quality should be framed within a robust data strategy and approached systematically. However, after many years in the field, experience has been showing me that for most of the cases this is not true.
?For every data quality issue, there is a business impact.
?Most likely 80% of the people I know working on IT will stick my picture on a dart board after reading this (at least they’ll end-up with prettier dart boards ??). Or you can just leave your comments and reactions on the article, it’s definitely more ecological.
?How many organizations, in every industry are suffering from poor implementations of the most basic management reporting?
How many are confronted with a situation where they simply accepting to live with mediocre, under-performing solutions?
?Most organizations seem to be falling short in their efforts to become data-driven.
?Some of the principles I believe essential, not just to recover data trust, but to effectively enable the potential of data driven insights in the organizational decision processes.
?How many business decisions are being made without the support of reliable data?
How many business decisions are being made without any data support at all?
?75% of organizations involved in digital transformation efforts are confronted with a situation where they simply assume that the investment was wasted and worse than that, accept to live with mediocre, under-performing solutions – expensive failures.
?Its’ paradoxical. As we see organizations struggling to collect as much data as possible, we see infrastructure, storage, processing, and analysis investments increasing and at the same time we witness the quality of analysis and insights decreasing.
?Most organizations are working with an average of 15 to 20 customer data sources, with customer data scattered across multiple systems and formats. With so many data sources spread throughout the organization, and confined to functional and channel-specific silos, building a single view of the customer is a huge challenge.
?My contribution to Africa Business Communities Forum, on the question "What are the opportunities for the African Tech Industry in 2021?"
January 5, 2021
?Making the option for data quality initiatives that are more focused and efficient creates and increases the awareness across the enterprise and ends acting as the motor from within the organization for a full Data Quality Strategy program.