The Ultimate Guide by SIFT Analytics
Why do data analytics initiatives succeed, and others fail? When deploying data analytics, it comes with numerous challenges that can hinder successful implementation and achieving goals. This guide simplifies these challenges and provides insights into overcoming them. However, if you are encountering a specific challenge that is not covered here, feel free to reach out to SIFT Analytics.
Data literacy involves understanding data sources, infrastructure, analytical methods, and the ability to describe scenarios and resulting business outcomes. Improving data literacy within an organization is crucial for effective data analytics. SIFT periodically conducts data literacy workshops that you can and should request to enhance your team’s skills.
👉 Request for a Data Literacy Workshop
Even with powerful no-code analytics tools designed for business users, some technical knowledge and skills are necessary. These tools enable users to focus on interpreting data, refining strategies, and making informed decisions critical to the business’s success. Continuous training and upskilling are essential to keep pace with evolving tools and technologies.
Choosing the right data analytics tool is challenging as no single tool fits every need. Popular analytics tools vary, and selecting the right one involves assessing many factors in line with your organization’s needs.
So, how do you identify one that’s a good fit for your company? Start by considering your organization’s business needs and knowing who will be using this analytics tool. Will it be used by data scientists, data analysts, or by non-technical users who need an intuitive interface, or should it suit both kinds of users? Some platforms provide an interactive experience for iterating on code development while others focus more on point-and-click for less technical users.
Next, what are your goals and which stage are you at in the digital transformation journey? Are you considering building real-time analytics, or do you have many incoming data through different sources where you want what we call one source of truth, ensuring data quality, governance, or migrating your data from one platform to another or automation?
You see, there are many goals. Some platforms specialize in some areas, while others can deliver seamless end-to-end implementation. So you will have to do intensive research, and the best way to start finding information is by reaching out to consultancy like SIFT Analytics to guide and provide you with proven strategies and a roadmap.
Finally, consider price and licensing. If you need advice, #AskSIFT
Advanced analytics, including Artificial Intelligence (AI), Machine learning (ML), Business Process Automation, Data Integration and Migration, Data Mining, and more complex analysis.
There are powerful tools to deploy advanced analytics; however, there is a need for data managers and subject matter experts, and management to be very involved to ensure that the goals are aligned and the suitable strategy.
There are a few things to take care of before evaluating the available tools. You should first understand the types of data your enterprise wants to analyze and, by extension, your data integration requirements. In addition, before you can begin analyzing data, you’ll need to select data sources and the tables and columns within them and replicate them to a data warehouse to create a single source of truth for analytics.
You’ll want to assess data security and data governance as well. If data is shared between departments, for example, there should be access control and permission systems to protect sensitive information.
Companies often have data in various systems (e.g., CRM, Excel, social media, SAP, POS). Manually combining this data into one source can be time-consuming and error-prone.
👉 Find out more about Data Integration here
Maintaining high data quality is crucial. Challenges include dealing with incomplete, inconsistent, or inaccurate data. Implementing robust data quality management processes and tools is essential for reliable analytics.
Machine learning involves selecting the right algorithms, handling large datasets, and ensuring model accuracy. Continuous monitoring and updating of models are necessary to adapt to new data and maintain performance.
Ensuring data security and privacy is critical, especially with increasing regulatory requirements. Implementing strong security measures and compliance protocols is essential to protect sensitive data.
#AskSIFT
Need help with any of these challenges? Reach out to SIFT Analytics for expert guidance and solutions.
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As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
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Published by SIFT Analytics
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SIFT Analytics Group
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