How do you change your data analytics for new needs?

Some of the very basic steps that I follow: 1. Understand business need 2. Gather requirements like files/accesses/data/targets/methodology/point of contacts 3. Proof of concept/ feasibility study/ relevancy of new methodology using the existing or new tool/license 4. Define the scope/objectives 5. Allocate the resources and define the timelines 6. Define the priorities 7. Setup routine meetings to monitor status and updates and to tackle challenges 8. UAT and updates 9. Delivery and feedback

Changing your data analytics involves re-evaluating your business objectives, reassessing the metrics and KPIs that you are tracking. Review your data sources, checking for new technology or platforms that aim to solve your specific pain point. EG. Adding a Customer Data Platform (CDP) for data tracking. Review the skills & training to ensure that your company has the resources to support the new analytics strategy. Collaborate across departments to understand different challenges and data needs. This holistic approach will ensure that analytics solutions are relevant and comprehensive. Also, getting buy-in across your organization will increase the likelihood of success.

To adapt data analytics for new needs, follow these steps: assess current requirements, review existing analytics processes, evaluate data sources, update technology stack, assess team skills, define key performance indicators (KPIs), adopt an iterative approach, maintain open communication with stakeholders, ensure security and compliance, document changes, establish a feedback mechanism, stay informed about industry trends, design analytics solutions with scalability, and foster collaboration between data analysts, domain experts, and business leaders. By implementing these steps, you can optimize your analytics processes to meet new and evolving business needs effectively. Maintaining open communication, reviewing security measures.

Changing your data analytics for new needs involves a systematic process to adapt your data analysis approach to meet evolving business requirements. Start by clearly defining and understanding the new needs or objectives that require adjustments to your data analytics. This might involve changes in business goals, market conditions, or customer preferences. Stay updated with industry trends, technologies, and best practices. Continuously assess and refine your data analytics strategy to stay aligned with changing needs.

Businesses are under constant evolution and we need to be part of an actively developing environment, like a story that is constantly being written with new chapters emerging from challenges and tribulations. Sometimes even *considering* change, looking at new platforms or emerging technologies or adding a new skill to the team can give new perspectives to old challenges.

The key in Situation Assessment is gathering information, feedback from the users of your analyses on their frequency of use, ease of use and the added value. If these analyses are hosted online, then you can collect some of this information automatically. It is also important to follow the "external world" through a variety of channels so you remain up-to-date. An effective way of doing this is to gather information about competitors via your end users, if possible.

My personal experience vividly illustrates the adaptability required in data analytics projects when confronted with evolving business needs. We were initially focused on a research goal: measuring the impact of a customer's employment status change on their credit score. As we delved into exploratory data analysis (EDA) and hypothesis testing, we unearthed insightful findings that piqued the business's interest. They requested the inclusion of income effects and other dimensions in our research, even though these weren't our primary objectives. In response, we swiftly adjusted our approach to accommodate these new requirements. Hence, Agility in Data Analytics, Interdisciplinary Collaboration, and Balancing Scope are really helpful.

Stakeholder Alignment: Engage with key stakeholders to understand evolving business goals and KPIs. Competitor Analysis: Conduct thorough competitor analysis to identify industry benchmarks and emerging trends. Tool & Method Review: Evaluate existing analytics tools and methods; identify gaps and areas for enhancement. Performance Audit: Analyze past data analytics performance; discern successful strategies and pinpoint areas for refinement. Environmental Scan: Stay abreast of external factors impacting the industry; adapt analytics strategies accordingly. Define Objectives: Clearly outline scope, objectives, and priorities for data analytics transformation aligned with business goals.

Data Analytics is one of those areas which is changing quickly from a tool perspective, to the business needs. Even data platforms are not stagnant, with vendors leapfrogging each other. We need to be careful since there is so much change, from business trends, to new technical capabilities, that we can get caught up always being "building" and not "executing" to get enough business value. So, assess, not just for tomorrow, but as far as you can. Look ahead and consider the strategic building blocks (example: AI which requires good data quality), and you want to keep working strategically while addressing tactical needs.

Requirements gathering is a skill that is often misplaced when thinking about Data roles, particularly when applying as a candidate or when hiring for a role in the area. Learn how to communicate effectively with various business units and senior stakeholders leading them, it's the best to clearly define changing needs and situations to set new objectives and priorities.

Change usually comes with extra work. You can't simply stop what you are doing now and start working on the changes and release the new version when it's ready; the world doesn't stop because you're changing something. In order to keep the additional workload to a minimum, it's critical to keep the scope of the change you will introduce at every step as narrow as possible. In other words, it's better to plan 5 small changes than 1 massive change.

Planning is critical, but remember there is two types of planning to consider, the strategic roadmap, and the individual initiatives. The strategic roadmap is important since it addresses the longer-term items. Data Analytics doesn't live in a silo (in fact we want to break them down), and in many cases, may connect to Data Quality initiatives (which can take years in some organizations). This doesn't take away for the needed initiative project planning, which is (usually) more short-term, but involves both the technical and business side to gain value. Plot your path strategically, make smaller and tactical plans which fit in that strategic path. Change strategy when needed (hopefully infrequently).

The planning phase. Once you've understood what has changed and why by gathering requirements, it's time to plan. It's important to remember that there is still ongoing change/work and you simply can't stop that momentum to start something new. So, you need to understand how you communicate changes of scope to stakeholders and, in turn, manage their expectations. The key is visualisation. Delivering high-impact, high-value reporting insights is a great way to communicate scope and objective changes. Using live dashboards will help automate your tasks in keeping your stakeholders up to date and... happy!

In order to ensure the change you implement will not disrupt the work of your end users, it's better to run a Beta version before you roll out the changes officially. For the Beta version, it's more efficient if you pick an end user that is willing to take the time to test different scenarios, use cases so you can be as prepared as possible.

Involve who you can, as much as you can. When too many Stakeholders cause an issue, this points to a clarity or governance problem that should be solved before continuing. One challenge I have seen is Data Analytics, at least partially, is a reflection of business process. Changes may assist one team (who knows their process), but not all regions, and certainly not address all of the upstream and downstream impacts. The more stakeholders are identified and involved, the less likely it will be a change needs to be pulled back because of unintended consequences.

A great skill to master is learning how to build meaningful and user-friendly dashboards in popular tools such as Power BI and QlikView. Using Power BI as an example, you can build impactful reports and create metrics that are live, which means that automation can play a huge part in supporting what would be a highly administrative task. Mastering this skill also helps you understand what good looks like when translating business needs into valuable reports. Why? Because you'll know exactly what your stakeholders are looking for. All of this helps with your analytics and keeps change fluid.

Learning from the change is always better when the change is limited in scope and parallel to the "old" version. So, the work goes on while you are getting ready to make the change.

This is why measurement planning is a critical – and often overlooked – stage of the analytics workflow. If you don't have clear, measureable targets that align with business outcomes and stakeholder needs, how do you define what "success" looks like?

We make changes for a purpose, and if we just implement and leave, the business stakeholders will rarely see the value they are looking for. We need to keep learning and continuously improving the process or we will keep making the same mistakes over and over again.

The best way to learn from the change and how well you did in managing it is to ask for feedback from your peers and your team. Additionally, comparing the outcomes of the changes will help the business overall learn about how to approach future changes in the best way. Understand what worked, what didn't, what could be enhanced, and so on.

It can be overwhelming to think through the iterative nature of data analytics as a whole where you are increasing your knowledge, developing new skills, and becoming a subject matter expert for your organization. Oh, and you are doing this while providing deliverables on current projects! This can lead you to be inflexible; however, if you develop a habitual framework where you are constantly learning and applying new knowledge to hone your skills based on the needs of the business and your personal interests it can make adapting to the changing currents of data analytics much easier. A first step towards this is looking at a common task you are assigned and saying, "How can I improve this?"

This is the longest straw, and the move to a true data-driven organization and increasing the data literacy isn't something that happens overnight. Adapting to change is easy for some organizations, and not for others. The smoother you can make the path forward (and harder to go backwards), the better!

In the dynamic field of data analytics, staying relevant and effective requires a proactive approach to adapt to changing needs. To do this, organizations must continually assess their data sources, tools, and methodologies. This involves staying informed about emerging technologies and industry trends, regularly reviewing and updating data collection processes, and fostering a culture of data-driven decision-making. Additionally, flexibility in data analytics teams is crucial, enabling them to pivot quickly and allocate resources to address new business challenges or opportunities as they arise. By embracing change and remaining agile, data analytics can continually evolve to meet the evolving needs of the organization.

Following nicely on from my previous point, learning and continuously improving will set the business up for successful adaptation for future changes, big or small. This will inevitably decrease the cost and time spent on the changes, leaving you with more time to promote a collaborative and high-quality data culture within the business, encouraging innovation and SMART objectives!

The shelf-life for analytics skills is short, and getting shorter. With the emergence of new tools and technology (particularly AI), it's never been more important to adopt an "always learning" mentality to stay ahead of the skills curve.

I would start with: 1. Reevaluating/Assessing the current Analytics and data to list down what 'new needs' to be done. Will try to find gap. 2. Now I will start with data exploration to know what new data I require. 3. Will test and try new analytics software or techniques to view reports in a different angle 4. Will work on Automation to improve the whole analysis process and efficiency. 5. Will continuously monitor the performance and gather feedback from all relevant stakeholders 6. Will work on m compliance and security. Will ensure about best practices. 7. Data analytics is an ongoing process. Will iterate and evolve. Will stay agile for future iterations.

To adapt analytics for new needs in digital marketing, it's crucial to follow these steps: 1. Define Clear Objectives: Understand the new project's specific goals. 2. Identify Key Metrics: Choose relevant metrics that align with these objectives. 3. Implement New Tracking: Set up or adjust tracking tools to capture essential data. 4. Collect and Analyze Data: Continuously gather and analyze data for insights. 5. Report and Visualize: Create clear reports and visualizations. 6. Iterate and Optimize: Make data-driven adjustments for optimization. 7. Stay Informed: Keep up with industry trends and new analytics tools. These steps will help you customize analytics to meet new needs effectively.

I think first of all we need to accept that analysis needs WILL change. It's not about what went wrong or why it changed... you improve what you focus on. So you build an analysis for XYZ, 2 yrs later those KPIs have improved and now you want to focus your attention on ABC. The real question is how do you build your data and visuals to be sustainable and dynamic so that these changes in focus do not take months to adjust to but just a few hours.

Identify the evolving requirements and objectives. Review existing data sources and tools for relevance. Gather additional data if necessary. Adjust analysis methods and models.

In this constantly changing world of Analytics, its very important to invest in training and developing the skills of the analytics team to keep them abreast of the latest methodologies and technologies. Stay current with analytics tools and technologies. Adopt new tools that better suit the evolving requirements.

Adapting data analytics for new needs involves tweaking methodologies and focusing on relevant metrics. Imagine your data analytics as a wardrobe – when trends change, you don't cling to bell-bottoms. Similarly, for evolving needs, shift from basic sales figures to predictive analytics. For instance, a retail business might transition from just tracking purchases to forecasting demand using machine learning, anticipating customer preferences for smarter inventory management. It's like upgrading from outdated fashion to the latest analytics chic!