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Quality Over Quantity: 5 Steps to Cleaner, More Effective Data Managing vast amounts of data is daunting. With millions of records to handle, maintaining high data quality, compliance with regulations, and streamlining processes can seem overwhelming. Yet these tasks are essential for any organization striving to stay competitive and efficient. In fact, data quality is a top priority for analytics and IT leaders.? Every company aspires for the best data quality, which includes the retention and use of relevant, clean data while also following key regulatory requirements. But ensuring data quality in an easy and automated fashion is extremely difficult. With new regulations being introduced at an increasing rate alongside the scale of customer data, companies need to automate processes – including the removal of stale data. Keeping data clean and lean Discover strategies for assessing and improving your data quality on Trailhead, Salesforce’s free online learning platform. Start learning +700 points Module Data Quality Learn the ways of this trail. What is data quality and why is it important? We know data quality is critical for effective data management and decision-making, but what exactly are we talking about when we use the term “data quality”?? According to the Data Management Association (DAMA), data quality is defined as “the degree to which data is accurate, complete, reliable, and relevant to the purpose for which it is used.”? This definition highlights the essential dimensions of data quality that organizations must have to derive meaningful insights and make informed choices. And it’s no secret that good data quality is the foundation of any successful business. In simpler terms: bad data in, bad results out. Speaking of bad data, it’s more than just a nuisance – it comes with a hefty price tag. Poor data quality is estimated to cost organizations an average of $12.9 million, underscoring the significant financial burden poor data quality poses on organizations. Salesforce research also indicates that 80% of business leaders consider data essential in decision-making, positioning it as a critical driver in strategic choices.? Additionally, customer trust is closely tied to how data is utilized; 71% of customers are more likely to trust companies that clearly communicate their data usage practices. Together, these statistics highlight the essential role of high-quality data in driving business success and elevating customer relationships. Help your customer success team shine with a stellar data quality approach Data quality plays a pivotal role in allowing customer success teams to perform at their best. Having the best data empowers the best experiences for these teams, including: Time savings: Your teams are no longer searching for what they need, because it’s already at their fingertips Increased efficiency: Achieve better resource utilization and operational efficiency when teams aren’t having to sift...

While data leaders focus on the newest technology, they forget the operating model is really the difference maker #DataStrategy #DataOperatingModel #DataEcosystem #Datagovernance

Does Data Driven Decision Making (DDDM) important in Modern Work culture? Data-Driven Decision Making (DDDM) is increasingly necessary for organizations in today’s competitive, fast-paced, and information-rich environment. Let’s discover the meaning of Data Driven Decision Making (DDDM) and its basics. Data-Driven Decision Making (DDDM) refers to the process of decision making is based on the analysis of massive volumes of data. IT is defined as using facts, metrics, and insights to guide strategic business decisions that align with goals, strategies, and initiatives. In a data-driven approach, decisions are grounded in solid evidence derived from various data sources, such as business performance metrics, customer behavior, market trends, or operational data. Now let us understand how the process works and becomes benefited in modern organizational culture. The process of data-driven decision making (DDDM) involves several structured steps that help organizations gather, analyze, and apply data effectively to make informed decisions. Define the Problem or Goal: Firstly, we need to be very cleared about problem we want to solve or the goal we want to achieve. This step ensures that your data collection and analysis will be focused and relevant as per our requirement. Collect Relevant Data: Data can come from internal sources like (sales records, customer databases, website analytics) or external sources (market research, social media trends, industry reports). However, the data we collect should ensure the data is clean, consistent, and relevant. Clean and Prepare the Data: This step involves removing duplicates, handling missing values, correcting errors, and ensuring that the data is in a usable format. Data Analysis: Data analysis can range from simple descriptive statistics to more complex predictive analytics such as Descriptive, Diagnostic to discover the cause of ‘why something happened?’?Predictive like and forecasting and Prescriptive the recommends action that includes decision making step. Interpret the Results: Next Step that needs to be followed is Interpret the Results. t’s important to translate complex data into something meaningful for decision-makers. We get the clarification about the key insights that can inform decision-making aligns with business objectives. This process helps an organization from various aspects as with a deep understanding of data, companies can make better predictions about future trends and outcomes. Data helps businesses tailor their offerings and strategies to better meet the needs and achieve the goal. data-driven decision making empowers organizations to make smarter, more informed choices, helping stay competitive, reduce risks, and improve overall performance. #datadrivendecisionmaking #mordernworkprocess #hrcommunity #organizationalculture

While this article adeptly highlights the critical role of data as a strategic asset and the transformative potential of computational governance, it may underplay the inherent challenges in transitioning to such a model. The promise of streamlined data governance and enhanced business agility through computational governance is compelling. However, many organizations may face significant hurdles in implementation, including the integration of legacy systems, the need for substantial cultural change, and the resource-intensive nature of setting up automated governance frameworks. Without addressing these practical considerations, the discussion risks seeming overly optimistic about the ease and speed of adopting such advanced data governance solutions. https://lnkd.in/dB5Hrppi Follow Jose Almeida for your content on Driving Better Insights Through Better Data Reach out today for consultations and advisory services: ?? Email: [email protected] ? #DataManagement #DataGovernance #MasterDataManagement #DataQuality #DataStrategy #BusinessIntelligence #BigData #Analytics #DigitalTransformation #DataConsulting #DataInsights #DataAnalytics #DataScience #DataSecurity #InformationManagement #DataInnovation #AgileData #TechConsulting #GlobalData #BusinessStrategy #innovation #management #humanresources #digitalmarketing #technology #creativity #future #futurism #entrepreneurship #careers #markets #startups #marketing #socialmedia #venturecapital #socialnetworking #leanstartups #branding #advertisingandmarketing #motivation #personaldevelopment #investing #jobinterviews #sustainability #personalbranding #education #customerrelations #productivity #sales #socialentrepreneurship

While data leaders focus on the newest technology, they forget the operating model is really the difference maker And I've never seen a bigger problem within data teams than a lack of operating model This is a crucial framework for guiding how the data and analytics function is organised and operates to deliver value within an organisation. To dive into more detail, I have mapped out three main levels in an Operating Model, each with key components worth noting: 1. ???????? ???????????? ???? – ??????-????-?????? ???????????????????? ?? ?????????? & ????????????????????????????????: Defining the purpose of each role, the decisions they make, and their accountabilities. This clarity eliminates inefficiencies and sets a clear direction for individuals and teams, necessary in complex data operational structures. 2. ?????????????????? ???? ???????????????? – ?????????????? ?????? ?????????????????????? ???????????????? ?? ?????????????????? ??????????: Establish a clear reporting structure within the data team and their interaction with other business functions. In large organizations, knowing who reports to whom is vital for maintaining timelines, ownership, and delivery. ??? ???????????????? ??????????: Define how teams deliver data initiatives from start to finish (e.g., agile, waterfall, etc.), including how they work across capabilities/ teams ?? ???????????????? & ???????????????? ??????????????????: Implement processes for collaborating with data and business stakeholders. Design to alleviate existing pain points and promote collaboration 3. ?????????????????? & ?????????????????? – ?????????????????? ???????? ?????? & ???????????????????? ?????????? ?? ??????????????/ ?????? ????????????????????: Designate individuals with accountability to ensure the overall direction is clear and consistent ?????????? ???????????????? & ???????? ??????????: Ensure the ‘what’ and ‘where’ is defined when it comes to the data direction ???????????????????????? ????????????: Establish forums to discuss issues, align on progress/ direction and determine the best path forward ?????? ?????????? ????????????????????: Set guiding principles (kind of like values) for how teams should work together in respectful, collaborative and efficient ways ?? ?????????????????????? ????????????????????: Define KPIs and ROI for data initiatives. Set up points to regularly measure progress and performance to ensure teams remain focused Check out more in my article on this topic from the summer! #DataStrategy #DataOperatingModel #DataEcosystem #DylanDecodes

Does Data Driven Decision Making (DDDM) important in Modern Work culture? Data-Driven Decision Making (DDDM) is increasingly necessary for organizations in today’s competitive, fast-paced, and information-rich environment. Let’s discover the meaning of Data Driven Decision Making (DDDM) and its basics. Data-Driven Decision Making (DDDM) refers to the process of decision making is based on the analysis of massive volumes of data. IT is defined as using facts, metrics, and insights to guide strategic business decisions that align with goals, strategies, and initiatives. In a data-driven approach, decisions are grounded in solid evidence derived from various data sources, such as business performance metrics, customer behavior, market trends, or operational data. Now let us understand how the process works and becomes benefited in modern organizational culture. The process of data-driven decision making (DDDM) involves several structured steps that help organizations gather, analyze, and apply data effectively to make informed decisions. Define the Problem or Goal: Firstly, we need to be very cleared about problem we want to solve or the goal we want to achieve. This step ensures that your data collection and analysis will be focused and relevant as per our requirement. Collect Relevant Data: Data can come from internal sources like (sales records, customer databases, website analytics) or external sources (market research, social media trends, industry reports). However, the data we collect should ensure the data is clean, consistent, and relevant. Clean and Prepare the Data: This step involves removing duplicates, handling missing values, correcting errors, and ensuring that the data is in a usable format. Data Analysis: Data analysis can range from simple descriptive statistics to more complex predictive analytics such as Descriptive, Diagnostic to discover the cause of ‘why something happened?’ ?Predictive like and forecasting and Prescriptive the recommends action that includes decision making step. Interpret the Results: Next Step that needs to be followed is Interpret the Results. t’s important to translate complex data into something meaningful for decision-makers. We get the clarification about the key insights that can inform decision-making aligns with business objectives. This process helps an organization from various aspects as with a deep understanding of data, companies can make better predictions about future trends and outcomes. Data helps businesses tailor their offerings and strategies to better meet the needs and achieve the goal. data-driven decision making empowers organizations to make smarter, more informed choices, helping stay competitive, reduce risks, and improve overall performance. #datadrivendecisionmaking #mordernworkprocess #hrcommunity #organizationalculture

Most teams see governance as the data traffic cop, keeping things slow and centralized??. But as data demands skyrocket and AI accelerates, traditional governance just isn’t enough. Imagine if each team could manage its own data but still follow a shared, adaptable rulebook—one that's agile for individual domains yet unified across the organization. But what and how? This is what a recent article by Charlotte Ledoux & Vivek Dubey can help you find out! ? This article dives into a transformative shift in data governance driven by the Data Products approach, which enables data teams to achieve both flexibility and governance in managing data across an organization. It covers the key themes of how ???????????? ?????????????????? gives organisations control to teams over their data products to ensure that data governance reflects each team’s unique context and expertise.????? Building a dedicated ???????????????? ?????????? is another crucial aspect that ties data governance to real-world applications, helping teams develop a shared ?????????????????????????? ???? ???????? ??????????????????????, ????????????????????, ?????? ??????????????????????????. For example, it addresses the challenges of defining and categorizing data elements in a way that is consistent across the organization but also adaptable to each domain’s specific needs. And most interestingly, the article addresses a crucial question: Are there technologies to support decentralized governance? It highlights the “???????? ?????????????? ???? ?? ??????????” approach, using a unified platform with a central catalog, data quality tools, and automation to streamline governance without duplicating efforts. ???? Access the full article on Medium to learn more about this: https://lnkd.in/gDtMJpcq

Never forget that data governance is developed to protect not only data consumers and customers but also the business.

This is an all too common problem. Business people don't want to engage fully with the data in the way that data people want them to, but need the information the analytics and visualisations provide to make their decisions. So analytics teams of various guises get set up and the burden falls on them in the way the OP describes and these people become one or all of 1.) a bottle neck; 2.) scapegoats; 3.) "data monkeys". To break this is the problem, and the one that IMHO needs a carrot-and-stick approach that Dr. Sebastian Wernicke describes. I am minded to say that unless the seniors in the organisation give you a mandate to call on them to enforce the data management regime if resistance is encountered, then you are doomed to failure. So it falls to the teams to educate and keep educating the business people they encounter to the need for them to have a better understanding of what goes on and the issues that hinder their work that the 'business' need to address to mutual benefit. This often does need to be dressed up in a non-techy way and go fully on the WIIFM angle, but it does only take a small number of people beyond the 'techies' to grasp the significance to make a real difference.

??????????????????? ?? ????????-???????????? ??????????????: ???????????? ???????????????????? ?????? ???????????????? ?? Creating a data-driven culture is a transformative journey, but it comes with its own set of challenges. Based on my first-hand experience, here are some common issues data teams face and how to overcome them: ?? ???????????? ???????????????????? 1. Data Silos: One of the biggest hurdles is dealing with data silos. Different departments often store data in isolated systems, making it hard to get a unified view. Breaking down these silos requires a concerted effort to integrate data across the organisation. 2. Lack of Data Literacy: Not everyone is comfortable with data. I've seen teams struggle because they lack the skills to interpret and use data effectively. Investing in training and promoting data literacy is essential to empower everyone to make data-driven decisions. 3. Resistance to Change: Cultural shifts are never easy. There’s often resistance from teams accustomed to traditional decision-making methods. It’s crucial to communicate the benefits of a data-driven approach clearly and involve key stakeholders early in the process. 4. Data Quality Issues: Inconsistent and inaccurate data can undermine trust in data-driven insights. Ensuring high data quality through proper governance and validation processes is vital for reliable decision-making. 5. Tool Overload: With so many data tools available, it’s easy to fall into the trap of adopting too many, leading to confusion and inefficiency. Selecting the right tools that align with your needs and ensuring proper training can help avoid this pitfall. ?? ???????????????????? ?????? ???????????????????? 1. Foster Collaboration: Encourage departments to share data and insights. Creating cross-functional teams can help bridge gaps and foster a more integrated approach to data. 2. Promote Data Literacy: Regular training sessions and workshops can help boost data literacy across the organization. Make sure everyone understands how to access, interpret, and use data effectively. 3. Lead by Example: Leaders need to champion the use of data in decision-making. By demonstrating the value of data-driven decisions, leaders can inspire their teams to follow suit. 4. Ensure Data Quality: Implement robust data governance practices to maintain data quality. Regular audits and validation processes can help ensure that the data used for decision-making is accurate and reliable. 5. Streamline Tools and Processes: ?Choose tools that integrate well with your existing systems and meet your specific needs. Providing comprehensive training on these tools can help teams use them more effectively. Building a data-driven culture is a continuous process that requires dedication and effort. By addressing these common challenges head-on, we can create an environment where data is a trusted and valuable asset for everyone. #DataDrivenCulture #TechLeadership #DataLiteracy #DataGovernance