Dresner Advisory Services, LLC’s Post

Upsurge in SI and Consultant Use on BI Projects Requires a Safety Net. Our latest data show that use and perceived importance of system integrators (SIs) and consultants continues to rise. Organizations increasingly allocate greater percentages of their data and analytics project budgets to the use of SIs and consultants. In addition, expertise matters most when choosing an SI or consultant. And when sourcing that talent, organizations increasingly consider their vendors as trusted advisors that can direct them to the right potential SI and consultant resources to use. Hiring SIs and consultants for an increasing percentage of project responsibilities requires greater expertise in managing the engagements. But in almost no instances—even for the most critical projects—should a data leader look to manage these engagements directly. Instead, a data leader should coordinate as needed with project managers but still primarily focus on influencing the foundations for and use of data and analytics throughout the organization. These data and analytics “guardrails” include data governance principles and programs; data management initiatives such as data integration, data quality, and master data management; guidance on where and when certain analytical techniques are appropriate and which data (and other) sources to use; as well as strategies regarding data availability and use in generative AI. Such prioritization make sense for a data leader—staff handles the granular detail of managing specific (often technically oriented) engagements while the data leader guides proper focus on the principles of Hyper-Decisiveness while data leader guides proper focus on the principles of Hyper-Decisiveness while ensuring initiatives improve the Hyper-Decisive® maturity of the organization. Become a #DataLeader to continue reading this and many more critical thought leadership pieces - https://ow.ly/Rq4L50Sq0CU #businessintelligence #analytics #data #leadership #consultants #systemsintegrators

𝐖𝐡𝐲 𝐂𝐡𝐨𝐨𝐬𝐞 𝐚𝐧 𝐈𝐧𝐭𝐞𝐫𝐢𝐦 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭? All companies have one thing in common: They need to make sense of their data and strategically use it to improve operations and drive their business forward. But how do they achieve this? It’s simple: They have a defined data strategy that serves as the foundation for their data and analytics practices. A good data strategy is more than just about data and technology. It is a comprehensive plan that outlines the people, processes, technology, and data needed to meet organizational goals and drive informed business decisions. However, many organizations often lack the specialized skills required to create an effective data strategy. This is where interim managers come in. Hiring an interim manager to develop and manage data strategies is becoming increasingly common and for good reasons. ✔ 𝐈𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐚𝐧𝐝 𝐅𝐫𝐞𝐬𝐡 𝐏𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞𝐬: Interim managers bring a wealth of experience and a fresh, unbiased view to your data management challenges. They can quickly assess your needs and implement best practices tailored to your organization. ✔ 𝐑𝐚𝐩𝐢𝐝, 𝐈𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬: Interim managers are skilled at driving rapid transformations. Their focused approach ensures that strategic changes in data management are implemented efficiently, allowing your company to navigate complex data landscapes with ease. ✔ 𝐀𝐠𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: According to the Institute of Interim Management's Survey 2023, there is a high demand for interim managers in data management roles. This approach helps organizations remain agile and responsive to market dynamics without the long-term commitment of permanent hires. ✔ 𝐂𝐨𝐬𝐭-𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐧𝐞𝐬𝐬: Interim managers offer a cost-effective solution for high-level expertise without the overhead costs associated with permanent employees. This financial flexibility allows companies to allocate resources more efficiently. ✔ 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐚𝐧𝐝 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: An interim manager not only develops and implements your data strategy but also ensures that your internal team is trained and capable of maintaining the strategy moving forward. This transfer of knowledge is invaluable for sustaining long-term success. By leveraging the expertise of interim managers, companies can ensure their data strategy is robust, comprehensive, and aligned with their business objectives. Whether you are starting from scratch or need to revamp an existing strategy, an interim manager can provide the expertise and leadership necessary to drive your data initiatives forward. Ready to develop your data strategy? Consider the benefits of hiring an interim manager to guide your organization through the complexities of data management and unlock the full potential of your data.

Do you have a team just getting started on your data analytics journey? Considering a transition to a new data platform or set of reporting tools? Not sure where to begin or if you have the right team in place? ENGIOTEK, LLC is a boutique data analytics and business intelligence consulting firm that services projects of all sizes with a goal to provide the best personalized service possible. We pride ourselves with fitting into your data team ready to assist you to reach your goals successfully, whether through staff augmentation or project leadership. ENGIOTEK specializes in: Data Engineering: ENGIOTEK designs and architects data pipelines, lakes, platforms, and products. We can help extract data from multiple source systems, enforce data quality standards, transform data for consistency, and deliver it in a format suitable for storage in data warehouses or application development. Data Warehousing: ENGIOTEK ensures historical data is properly modeled and maintained to support reporting of measures/facts by dimensions over time. Business Intelligence (BI): ENGIOTEK helps mitigate BI implementation challenges. We unlock hidden business opportunities and insights for clients by providing accurate data analytics and visualization services. Our team builds high-tech dashboards and visualizations to enhance decision-making. Data Visualization: ENGIOTEK focuses on visualizing data effectively. We create visual representations that go beyond tabular formats, making it easier to absorb information and draw insights. Reach out at gbishop@engiotek.com to schedule a consultation!

Driving Business Success Through Strategic Data Mapping In the contemporary business landscape, leveraging data effectively is crucial for organizations striving to maintain a competitive edge. However, managing data from diverse sources often presents challenges in terms of consistency, accuracy, and integration. Enter data mapping—a strategic solution offering significant returns on investment (ROI) for businesses. Beyond its role in ensuring data cleanliness, effective data mapping holds the key to unlocking enhanced decision-making, operational efficiency, and cost savings. Enhancing Data Quality: Data mapping establishes clear mappings between different systems, ensuring consistency and accuracy across datasets. By eliminating inconsistencies and errors, organizations can rely on trustworthy data for critical decision-making processes. Empowering Analytics and Reporting: Consistent data enables seamless integration across business functions, facilitating comprehensive analyses and reliable reporting. This empowers businesses to derive deeper insights from their data, driving informed decision-making and strategic initiatives. Simplifying Data Integration: Data mapping streamlines the integration of data from diverse sources into centralized repositories like data warehouses. This automation reduces manual data manipulation tasks, enhancing efficiency and minimizing the risk of errors associated with manual processes. Cost Reduction: Data inconsistencies often lead to costly errors and rework. Effective data mapping minimizes these issues, resulting in significant cost savings by reducing resources allocated to error correction and manual data manipulation. Calculating ROI: To measure the ROI of data mapping initiatives, organizations can focus on quantifying the following benefits: Cost Savings from Error Reduction: Track resources expended on correcting data errors before and after implementing data mapping. Efficiency Gains: Measure time saved by automating manual data manipulation tasks and streamlining data integration processes. Revenue Increase from Informed Decision-Making: Analyze the impact of data-driven insights on revenue-generating activities such as sales and marketing. In conclusion, data mapping is a transformative process that drives operational efficiency, enhances decision-making capabilities, and delivers tangible ROI for businesses. By prioritizing data mapping initiatives, organizations can harness the full potential of their data assets and stay ahead in today's competitive business landscape. In the era of data-driven decision-making, data mapping is not just a recommendation—it's a strategic investment essential for organizations aiming to thrive in the information age. #BusinessAnalysis #DataAnalysis #DataMapping #ProcessImprovement #RequirementsGathering #ProjectManagement #HealthcareITClinicalDataAnalysis #HealthcareWorkflow #RegulatoryCompliance #EHR #OpenToWork #JobSeeking #BusinessAnalystJobs

Optimize Your Business Processes with Data Integration Hello LinkedIn family! Today, I'd like to discuss why data integration is critical for businesses. What is Data Integration? Data integration is the process of combining data from different sources and formats into a consistent and usable structure. Especially in large and complex organizations, data is often dispersed across various departments, systems, and applications. Benefits of Data Integration: 🔄 Ensures Consistency: By harmonizing data from different systems, it eliminates inconsistencies and errors, allowing you to base your decisions on more reliable data. ⏱️ Saves Time: Automates manual data collection, transformation, and validation processes, enabling your employees to focus on more strategic tasks. 📈 Improves Decision-Making: Consolidating all data in one place and making it analyzable helps you gain insights to boost your business performance. Challenges in Data Integration: Data Quality: Data from different sources may vary in quality and format, requiring cleaning and transformation. Technological Compatibility: Integrating legacy and modern systems can present technical challenges. Security and Privacy: Ensuring the security and confidentiality of data during integration is crucial, necessitating appropriate security protocols. My Approach: Customized Solutions: I develop data integration strategies tailored to your business's specific needs. Technical Expertise: Utilizing modern technologies like API integrations, ETL processes, and data lakes to effectively consolidate your data. Data Quality Control: I use advanced data cleansing and validation techniques to ensure the accuracy and consistency of integrated data. If you're seeking professional support in data integration, feel free to reach out. Together, we can optimize your business processes and make the most of your data. If you have questions, feel free to share them in the comments! #dataintegration #datamanagement #businessoptimization #dataanalysis #datascience

#dataanalysislegacysystems #modernization To enhance data analysis productivity in data warehouse projects especially when you deal with legacy systems, consider the below strategies. By combining these strategies, you can create an environment that supports efficient and effective data analysis in data warehouse projects with the business team. 1. Collaborative Planning: Involve business stakeholders in project planning to align clear goals and expectations, ensuring data analysis meets business problem & needs. 2. Clear Requirements: Clearly define data requirements with the business team to minimize misunderstandings and streamline the analysis process. 3. Data Profiling & Cleanup: Begin by thoroughly profiling and cleaning up legacy data to ensure its accuracy and reliability, minimizing potential issues during analysis. 4. Metadata Management: Establish robust metadata management practices to track data lineage, dependencies, and transformations, aiding in transparency and understanding within the project. 5. User-Friendly Tools: Provide user-friendly data analysis tools that empower business users to explore and analyze data in the lgeacy systems to understand the domain attributes without extensive technical knowledge. 6. Training & Support: Offer training sessions to the business team on using data analysis tools effectively, and provide ongoing support to address any challenges. 7. Regular Communication: Foster open communication channels between the data and business teams to facilitate feedback, address concerns, and adapt to evolving business needs. 8. Data Quality Assurance: Prioritize data quality to ensure accurate and reliable insights, reducing the likelihood of errors and time spent on troubleshooting. 9. Automation: Identify opportunities for automation in data preparation and analysis processes to streamline workflows and boost productivity. 10. Documentation: Maintain thorough documentation for data analysis processes, ensuring transparency and enabling team members to understand and replicate analyses, fast tracking to onboard new team members.

4 types of Data Automation • Data Integration • Data Transformation • Data Loading • Data Analysis & Insights Data automation works by automating repetitive tasks and processes involved in managing, processing, and analyzing data. Here's an overview of how data automation typically works: Data Integration: Once the data is collected, it needs to be integrated from different sources into a unified dataset. Data integration involves merging, combining, and consolidating data from disparate sources to create a single source of truth. Automation tools and platforms help streamline this process by automatically aligning data schemas, resolving conflicts, and maintaining data consistency. Data Transformation: After integration, the raw data often needs to be transformed into a format that is suitable for analysis or storage. Data transformation involves applying various operations such as cleaning, filtering, aggregating, enriching, and formatting the data. Automation tools enable the automation of these transformation tasks, ensuring that they are performed consistently and efficiently. Data Loading: Once the data is transformed, it is loaded into a target database, data warehouse, or analytical system where it can be queried and analyzed. Data loading involves inserting the transformed data into the destination tables or storage repositories. Automation tools facilitate this process by automating data loading tasks, optimizing performance, and ensuring data integrity. Data Analysis and Reporting: With the data loaded into the target system, automated processes can be set up to perform various analytical tasks such as generating reports, dashboards, and visualizations, running predictive models, and performing statistical analysis. Automation tools enable the scheduling and execution of these analytical tasks regularly, providing stakeholders with timely insights and actionable information. #dataautomation #dataintegration #datatransformation #dataloading #dataanalysis #reporting #monitoring #automationtools #automatedtasks #maintenance #errors #analyticaltasksheet #predictivemodels #stakeholders #filtering #aggregating #formatting#efficiency #dataexchange #collaboration #dataconsistency #interoperability #resources#efficientdata #timelyintervention #data security #centralrepository #visualizationdashboard #stakeholders#finland #helsinki #deeplance #canada #usa

6 reasons why our clients love Notitia's "analytics as a managed service" > where our amazing people get to work alongside your amazing people, to fill an data analytics resourcing "gap" (aka: you tell us what you need + consider it 🌟 DONE 🌟) 💪 Value & flexibility Call or email us directly. Send us a list of tasks. Ask us to mentor your internal experts. We're flexible to our clients’ ways of working + needs. Ramping up + down hours, depending on your project work or resourcing requirements. 🎯 Breadth of experience Our clients access not one expert - but the whole Notitita collective. We all have different backgrounds, experience in tools + technology and specialities. Strong leadership + culture means that even though you may work closely with one of our people, you actually leverage the problem-solving ability + strength of the team. 🕛 Time saving Our people are experts in what they do - which means we do the work quickly + effectively. 👁 Fresh eyes It always helps to get that different opinion. Our clients often tell us that the process of introducing a fresh perspective helps to challenge assumptions + produces great results. 🥇 Industry leading Our team has worked across nearly every industry and sector, we’ve seen how organisations have produced outcomes from their data and technology transformations. We bring this wealth of insights into each client project + it means that we get to deliver true “out of the box” + industry leading processes and projects. 👯 Great culture, great people Our people are not only amazing at what they do, we handpick them for aligning with our core values, ability to work alongside others with diverse ways of thinking, having an open mindset and thirst for solving problems. Read to find out more about "analytics as a managed service" https://lnkd.in/gW2HXtEd

Business Intelligence: Business Intelligence (BI) refers to the processes, technologies, and tools used to collect, analyze, and present data in a format that supports informed decision-making and strategic planning within an organization. It involves transforming raw data into meaningful insights and actionable information that can drive business growth, improve efficiency, and enhance overall performance. The key elements of Business Intelligence include: 1. Data Sources: BI relies on various data sources, both internal and external to the organization. Internal sources may include transactional databases, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, spreadsheets, and other structured and unstructured data sources. External sources can include market research reports, industry data, social media feeds, and public data sets. 2. Data Integration: BI involves integrating data from multiple sources to create a consolidated view. This process may include data extraction, transformation, and loading (ETL) to ensure data consistency, accuracy, and uniformity. 3. Data Warehousing: Data warehousing involves storing and organizing large volumes of data in a centralized repository, often using a specialized database called a data warehouse. 4. Data Modeling: Data modeling is the process of defining the structure and relationships within the data to facilitate analysis and reporting. 5. Data Analysis: BI tools provide capabilities for analyzing data to uncover patterns, trends, and insights. This includes descriptive analytics (summarizing historical data), diagnostic analytics (identifying reasons for past performance), predictive analytics (forecasting future outcomes), and prescriptive analytics (suggesting optimal actions). 6. Reporting and Visualization: BI systems offer reporting and visualization capabilities to present data in a meaningful and intuitive. 7. Key Performance Indicators (KPIs): KPIs are measurable metrics that reflect the performance of a business or specific areas within it. BI systems allow organizations to define and track KPIs, providing real-time or near-real-time visibility into the performance of critical processes. 8. Data Mining: Data mining techniques are employed to discover hidden patterns, correlations, and relationships within large datasets. 9. Data Governance and Security: BI systems require robust data governance practices to ensure data quality, integrity, and security. This involves implementing data governance policies, establishing data access controls, and complying with regulatory requirements to protect sensitive information. 10. Collaboration and Self-Service: Modern BI platforms often promote collaboration and self-service capabilities, allowing users to access and analyze data independently. #digitalhealth #digitalstrategy #consulting #transformation #cloud

Business Intelligence: Business Intelligence (BI) refers to the processes, technologies, and tools used to collect, analyze, and present data in a format that supports informed decision-making and strategic planning within an organization. It involves transforming raw data into meaningful insights and actionable information that can drive business growth, improve efficiency, and enhance overall performance. The key elements of Business Intelligence include: 1. Data Sources: BI relies on various data sources, both internal and external to the organization. Internal sources may include transactional databases, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, spreadsheets, and other structured and unstructured data sources. External sources can include market research reports, industry data, social media feeds, and public data sets. 2. Data Integration: BI involves integrating data from multiple sources to create a consolidated view. This process may include data extraction, transformation, and loading (ETL) to ensure data consistency, accuracy, and uniformity. 3. Data Warehousing: Data warehousing involves storing and organizing large volumes of data in a centralized repository, often using a specialized database called a data warehouse. 4. Data Modeling: Data modeling is the process of defining the structure and relationships within the data to facilitate analysis and reporting. 5. Data Analysis: BI tools provide capabilities for analyzing data to uncover patterns, trends, and insights. This includes descriptive analytics (summarizing historical data), diagnostic analytics (identifying reasons for past performance), predictive analytics (forecasting future outcomes), and prescriptive analytics (suggesting optimal actions). 6. Reporting and Visualization: BI systems offer reporting and visualization capabilities to present data in a meaningful and intuitive. 7. Key Performance Indicators (KPIs): KPIs are measurable metrics that reflect the performance of a business or specific areas within it. BI systems allow organizations to define and track KPIs, providing real-time or near-real-time visibility into the performance of critical processes. 8. Data Mining: Data mining techniques are employed to discover hidden patterns, correlations, and relationships within large datasets. 9. Data Governance and Security: BI systems require robust data governance practices to ensure data quality, integrity, and security. This involves implementing data governance policies, establishing data access controls, and complying with regulatory requirements to protect sensitive information. 10. Collaboration and Self-Service: Modern BI platforms often promote collaboration and self-service capabilities, allowing users to access and analyze data independently. #digitalhealth #digitalstrategy #consulting #innovation