Predictive Analytics
Article | June 28, 2024
In the fast-paced world of data-driven decision-making, having a solid understanding of BI is essential. Explore a handpicked selection of top books that cover everything a data professional needs.
In today's data-driven landscape, businesses face the ongoing challenge of deriving meaningful insights from vast amounts of data to facilitate informed decision-making. This is where the field of business intelligence (BI) becomes crucial. Business intelligence encompasses a range of processes, technologies, and strategies that empower organizations to transform raw data into valuable insights, thereby driving business success. Access to comprehensive and insightful information is critical whether you're new to the field and seeking foundational knowledge or an experienced professional aiming to enhance your skills. This article will explore a curated selection of the best business intelligence books that offer valuable knowledge, practical guidance, and strategic insights. These books cover various facets of BI, including fundamental concepts, methodologies, agile approaches, data mining techniques, and cultural considerations. By delving into these essential resources, you will learn the tools and understanding necessary to navigate the complex realm of business intelligence, harness the full potential of your organization's data assets, and propel your business forward.
1. Business Intelligence
Elizabeth Vitt, Michael Luckevich, Stacia Misner
‘Business Intelligence, 1st Edition’ presents a comprehensive approach to empower readers with the vital knowledge and expertise needed to thrive in the dynamic field of business intelligence (BI). Co-authored by Elizabeth Vitt, Michael Luckevich, and Stacia Misner, this BI book is an invaluable resource catering to both beginners and seasoned professionals within the realm of business intelligence. Setting the stage, the book lays a solid foundation by illuminating fundamental concepts in business intelligence. It adeptly demonstrates how organizations can harness the power of massive data repositories to glean invaluable business insights, enabling them to make informed decisions swiftly and effectively regarding customers, partners, and operational aspects. 'Business Intelligence' guides readers through the intricacies of leveraging business intelligence insights to seamlessly amalgamate information, individuals, and cutting-edge technologies. Armed with this knowledge, readers gain the confidence to devise and execute successful business strategies with precision.
2. Business Intelligence: Data Mining and Optimization for Decision Making
Carlo Vercellis
In the book 'Business Intelligence: Data Mining and Optimization for Decision Making,' Carlo Vercellis explores the crucial intersection between data mining, optimization techniques, and decision-making processes within the field of business intelligence. The author starts by laying the groundwork for business intelligence and introduces key concepts such as data warehousing, data mining and its applications, machine learning, supply optimization models, decision support systems, and analytical methods for performance evaluation, setting the stage for a holistic approach to business intelligence. The book emphasizes the practical application of data mining and optimization techniques through real-world examples and case studies. 'Business Intelligence: Data Mining and Optimization for Decision Making' though aimed at postgraduate students, is an essential resource for professionals and researchers interested in harnessing the power of data mining, optimization, and decision support systems.
3. Business Intelligence: An Essential Beginner’s Guide to BI
Richard Hurley
'Business Intelligence: An Essential Beginner's Guide to BI, Big Data, Artificial Intelligence, Cybersecurity, Machine Learning, Data Science, Data Analytics, Social Media, and Internet Marketing,' provides a comprehensive introduction to the diverse and interconnected world of modern business intelligence. As the title suggests, this business intelligence book goes beyond traditional BI and explores emerging technologies shaping the business landscape. It delves into big data, providing insights into the challenges and opportunities associated with processing and analyzing massive datasets. Hurley introduces artificial intelligence (AI), machine learning (ML), and pattern recognition to explain their potential applications in driving business intelligence and decision-making. The book also addresses the critical roles that social media and internet marketing play in the growth of BI and how these platforms can be leveraged to gather valuable business intelligence insights and engage with customers effectively.
4. Business Intelligence: And How It Can Help You Grow Your Business
Johan Faerch
This book offers a practical and insightful guide to leveraging business intelligence strategies and techniques to drive business growth. This BI book is designed to empower entrepreneurs, business owners, and managers with the knowledge and tools necessary to make informed decisions and unlock the full potential of their organizations. It begins by introducing the concept of business intelligence and its significance in today's competitive marketplace and explains how BI goes beyond mere data analysis and reporting, acting as a catalyst for growth and innovation. The author highlights the transformative power of BI, demonstrating how it can provide a deep understanding of market trends, customer preferences, and internal operations. 'Business Intelligence: And How It Can Help You Grow Your Business' is a highly accessible and practical resource that bridges the gap between theory and real-world application. Johan Faerch's expertise and experience in the field, shines through as he provides valuable business intelligence insights and actionable strategies for harnessing the power of BI to achieve business growth.
5. Growing Business Intelligence
Larry Burns
'Growing Business Intelligence: An Agile Approach to Leveraging Data and Analytics for Maximum Business Value' offers a comprehensive guide to unlocking the full potential of business intelligence (BI) through an agile and adaptable framework. This BI book provides practical insights and strategies for effectively utilizing data and analytics to drive continuous growth and optimize business value. With a keen focus on core principles, the book highlights the importance of navigating the complexities of BI architecture to find the most suitable path for each unique organization. The book serves as a trusted resource, guiding readers on effectively managing the risks associated with disruptive technologies and adopting agile methodologies to deliver on the promises of BI and analytics in a rapid, concise, and iterative manner. 'Growing Business Intelligence' is an invaluable asset for business leaders, managers, and data professionals involved in BI, analytics, or Big Data projects. It also caters to organizations aiming to maximize the value derived from their data and investments in BI technology.
6 Business Intelligence: A Comprehensive Approach to Information Needs, Technologies, and Culture
Rimvydas Skyrius
'Business Intelligence: A Comprehensive Approach to Information Needs, Technologies, and Culture' by Rimvydas Skyrius delves deep into the multifaceted realm of business intelligence from various perspectives. This book looks at BI as a process driven by the synergistic blend of human capabilities and technological advancements and emphasizes the complex nature of information needs and decision-making support within organizations. It begins with a comprehensive introduction to the fundamental concepts of BI and related areas of information processing, navigating through the intricacies of BI, addressing data integration, information integration, and the processes & technologies involved. It further explores the maturity and agility of BI, delving into the components, drivers, and inhibitors of BI culture, as well as the soft factors like attention, sense, and trust that shape the BI landscape. Rimvydas, in this book, presents a holistic perspective view on business intelligence, possible structures and tradeoffs within the field of BI, providing readers with valuable insights.
7. Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence
John Boyer, Bill Frank, Brian Green, Tracy Harris, Kay Van De Vanter
'Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence' serves as a comprehensive and practical guide for organizations aiming to develop and implement a successful business intelligence strategy that drives excellence and delivers tangible results. The book begins by emphasizing the strategic importance of BI in modern organizations, highlighting its role in enabling informed decision-making, improving operational efficiency, and fostering a data-driven culture. It guides readers through the process of creating business alignment strategies that help prioritize business requirements, build organizational & cultural strategies, increase IT efficiency, and promote user adoption. The authors emphasize the importance of engaging stakeholders and fostering collaboration between business and IT teams to ensure the strategy's effectiveness and long-term success. 'Business Intelligence Strategy' equip readers with the right tools and strategies to develop and implement a robust BI strategy that drives excellence and delivers measurable value to their organization.
8. Fundamentals of Business Intelligence (Data-Centric Systems and Applications)
Wilfried Grossmann, Stefanie Rinderle-Ma
'Fundamentals of Business Intelligence' serves as a comprehensive and systematic introduction to the dynamic field of business intelligence , providing readers with strong foundational knowledge. This business intelligence book focuses on the transformation of process-oriented data into valuable information crucial for decision-making across diverse domains. The authors, Grossmann and Rinderle-Ma, follow a step-by-step approach to develop models and analytical tools that enable the acquisition of high-quality data structured in a manner conducive to applying complex analytical techniques. Covering a wide range of essential topics, the book delves into the fundamental concepts of business intelligence, the data-centric nature of BI, exploring various approaches to modeling in BI applications, data provisioning, data description, visualization, reporting, and more. The book seamlessly blends theoretical explanations with practical examples and compelling case studies to further enhance comprehension.
9. Business Intelligence: The Savvy Manager's Guide 1st Edition
David Loshin
'Business Intelligence: The Savvy Manager's Guide' is an insightful resource offering, practical guidance and strategic insight to assist managers in comprehending, implementing, and maximizing the benefits of BI. Loshin initiates the journey by clearly describing the fundamental architectural components of a business intelligence environment. Topics covered range from traditional subjects such as business process modeling and data modeling to more contemporary areas like business rule systems, data profiling, information compliance and data quality, data warehousing, and data mining. The book follows a logical progression, starting with the establishment of a robust data model infrastructure, followed by data preparation, analysis, integration, knowledge discovery, and ultimately the practical utilization of the acquired knowledge. Loshin adeptly provides clear explanations devoid of technical jargon, coupled with in-depth descriptions that articulate the business value of emerging technologies while offering the necessary introductory technical background. The true strength of this book lies in its ability to bridge the gap between technical and managerial perspectives.
10. Business Intelligence: The Savvy Manager's Guide 2nd Edition
David Loshin
'Business Intelligence: The Savvy Manager's Guide' by David Loshin is a comprehensive resource that equips managers with the knowledge and insights necessary to effectively navigate the field of business intelligence.The book covers the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge. The author also explores key factors to be taken into account in the planning and execution of a successful BI program, considerations for developing a BI roadmap, the platforms for analysis, such as data warehouses, and the concepts of business metadata. 'Business Intelligence: The Savvy Manager's Guide' serves as an accessible resource for BI professionals, including senior and middle-level managers, Chief Information Officers, Chief Data Officers, senior business executives, business staff members, database or software engineers, and business analysts seeking to harness the power of BI in their organizations.
Conclusion
The field of business intelligence is ever-evolving and plays a vital role in the current data-driven business landscape. The books highlighted in this article provide a wealth of knowledge and insights for individuals at various stages of their BI journey. These business intelligence books collectively offer a comprehensive and diverse range of perspectives on business intelligence to promote growth and expertise. Whether you are a beginner seeking fundamental knowledge or a seasoned professional aiming to enhance your skills, these resources provide valuable insights and guidance for harnessing the power of BI to drive success in the modern business landscape.
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Data Visualization
Article | March 15, 2024
When it comes to marketing today, big data analytics has become a powerful being. The raw material marketers need to make sense of the information they are presented with so they can do their jobs with accuracy and excellence. Big data is what empowers marketers to understand their customers based on any online action they take.
Thanks to the boom of big data, marketers have learned more about new marketing trends and preferences, and behaviors of the consumer. For example, marketers know what their customers are streaming to what groceries they are ordering, thanks to big data.
Data is readily available in abundance due to digital technology. Data is created through mobile phones, social media, digital ads, weblogs, electronic devices, and sensors attached through the internet of things (IoT).
Data analytics helps organizations discover newer markets, learn how new customers interact with online ads, and draw conclusions and effects of new strategies. Newer sophisticated marketing analytics software and analytics tools are now being used to determine consumers’ buying patterns and key influencers in decision-making and validate data marketing approaches that yield the best results.
With the integration of product management with data science, real-time data capture, and analytics, big data analytics is helping companies increase sales and improve the customer experience.
In this article, we will examine how big data analytics are transforming the marketing industry.
Personalized Marketing
Personalized Marketing has taken an essential place in direct marketing to the consumers. Greeting consumers with their first name whenever they visit the website, sending them promotional emails of their favorite products, or notifying them with personalized recipes based on their grocery shopping are some of the examples of data-driven marketing.
When marketers collect critical data marketing pieces about customers at different marketing touchpoints such as their interests, their name, what they like to listen to, what they order most, what they’d like to hear about, and who they want to hear from, this enables marketers to plan their campaigns strategically.
Marketers aim for churn prevention and onboarding new customers. With customer’s marketing touchpoints, these insights can be used to improve acquisition rates, drive brand loyalty, increase revenue per customer, and improve the effectiveness of products and services.
With these data marketing touchpoints, marketers can build an ideal customer profile. Furthermore, these customer profiles can help them strategize and execute personalized campaigns accordingly.
Predictive Analytics
Customer behavior can be traced by historical data, which is the best way to predict how customers would behave in the future. It allows companies to correctly predict which customers are interested in their products at the right time and place. Predictive analytics applies data mining, statistical techniques, machine learning, and artificial intelligence for data analysis and predict the customer’s future behavior and activities.
Take an example of an online grocery store. If a customer tends to buy healthy and sugar-free snacks from the store now, they will keep buying it in the future too.
This predictable behavior from the customer makes it easy for brands to capitalize on that and has been made easy by analytics tools. They can automate their sales and target the said customer. What they would be doing gives the customer chances to make “repeat purchases” based on their predictive behavior. Marketers can also suggest customers purchase products related to those repeat purchases to get them on board with new products.
Customer Segmentation
Customer segmentation means dividing your customers into strata to identify a specific pattern. For example, customers from a particular city may buy your products more than others, or customers from a certain age demographic prefer some products more than other age demographics.
Specific marketing analytics software can help you segment your audience. For example, you can gather data like specific interests, how many times they have visited a place, unique preferences, and demographics such as age, gender, work, and home location.
These insights are a golden opportunity for marketers to create bold campaigns optimizing their return on investment. They can cluster customers into specific groups and target these segments with highly relevant data marketing campaigns.
The main goal of customer segmentation is to identify any interesting information that can help them increase revenue and meet their goals. Effective customer segmentation can help marketers with:
• Identifying most profitable and least profitable customers
• Building loyal relationships
• Predicting customer patterns
• Pricing products accordingly
• Developing products based on their interests
Businesses continue to invest in collecting high-quality data for perfect customer segmentation, which results in successful efforts.
Optimized Ad Campaigns
Customers’ social media data like Facebook, LinkedIn, and Twitter makes it easier for marketers to create customized ad campaigns on a larger scale. This means that they can create specific ad campaigns for particular groups and successfully execute an ad campaign.
Big data also makes it easier for marketers to run ‘remarketing’ campaigns. Remarketing campaigns ads follow your customers online, wherever they browse, once they have visited your website.
Execution of an online ad campaign makes all the difference in its success. Chasing customers with paid ads can work as an effective strategy if executed well. According to the rule 7, prospective customers need to be exposed to an ad minimum of seven times before they make any move on it.
When creating online ad campaigns, do keep one thing in mind. Your customers should not feel as if they are being stalked when you make any remarketing campaigns. Space out your ads and their exposure, so they appear naturally rather than coming on as pushy.
Consumer Impact
Advancements in data science have vastly impacted consumers. Every move they make online is saved and measured. In addition, websites now use cookies to store consumer data, so whenever these consumers visit these websites, product lists based on their shopping habits pop up on the site.
Search engines and social media data enhance this. This data can be used to analyze their behavior patterns and market to them accordingly.
The information gained from search engines and social media can be used to influence consumers into staying loyal and help their businesses benefit from the same.
These implications can be frightening, like seeing personalized ads crop up on their Facebook page or search engine. However, when consumer data is so openly available to marketers, they need to use it wisely and safeguard it from falling into the wrong hands.
Fortunately, businesses are taking note and making sure that this information remains secure.
Conclusion
The future of marketing because of big data and analytics seems bright and optimistic. Businesses are collecting high-quality data in real-time and analyzing it with the help of machine learning and AI; the marketing world seems to be up for massive changes. Analytics are transforming marketing industry to a different level. And with sophisticated marketers behind the wheel, the sky is the only limit.
Frequently Asked Questions
Why is marketing analytics so important these days?
Marketing analytics helps us see how everything plays off each other, and decide how we might want to invest moving forward. Re-prioritizing how you spend your time, how you build out your team, and the resources you invest in channels and efforts are critical steps to achieving marketing team success.
What is the use of marketing analytics?
Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels.
Which companies use marketing analytics?
Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well.
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Business Intelligence, Big Data Management, Big Data
Article | August 17, 2023
We currently live in the age of data. It’s not just any kind of data, but big data. The current data sets have become huge, complicated, and quick, making it difficult for traditional business intelligence (BI) solutions to handle. These dated BI solutions are either unable to get the data, deal with the data, or understand the data. It is vital to handle the data aptly since data is everywhere and is being produced constantly.
Your organization needs to discover any hidden insights in your datasets. Going through all the data will be doable with the right tools like machine learning (ML) and augmented analytics.
According to Gartner, augmented analytics is the future of data analytics and defines it as:
“Augmented analytics uses machine learning/artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment.”
Augmented analytics is different from BI tools because ML technologies work behind the scenes continuously to learn and enhance results. Augmented analytics facilitates this process faster to derive insights from large amounts of structured and unstructured data to gain ML-based recommendations. In addition, it helps to find patterns in the data that usually go unnoticed, removes human bias, and allows predictive capabilities to inform an organization of what to do next.
Artificial intelligence has brought about an augmented analytics trend, and there has been a significant increase in the demand for augmented analytics.
Benefits of Augmented Analytics
Organizations now understand the benefits of augmented analytics which has led them to adopt it to deal with the increasing volume of structured and unstructured data. Oracle identified top four reasons organizations are opting for augmented analytics:
Data Democratization
Augmented data science availability to everyone has become a possibility thanks to augmented analytics. Augmented analytics solutions come prebuilt with models and algorithms, so data scientists are not needed to do this work. In addition, these augmented analytics models have user-friendly interfaces, making it easier for business users and executives to use them.
Quicker Decision-making
You will receive suggestions and recommendations through augmented analytics about which datasets to incorporate in analyses, alert users with dataset upgrades, and recommend new datasets when the results are not what the users expect. With just one click, augmented analytics provides precise forecasts and predictions on historical data.
Programmed Recommendations
Natural language processing (NLP) is featured on the augmented analytics platforms enabling non-technical users to question the source data easily. Interpreting the complex data into text with intelligent recommendations is automated by natural language generation (NLG), thus speeding up the analytic insights. Anyone using the tools can find out hidden patterns and predict trends to optimize the time it takes to go from data to insights to decisions using automated recommendations for data improvement and visualization. Non-expert users can use NLP technology to make sense of large amounts of data. Users can ask doubts about data using typical business terms. The software will find and question the correct data, making the results easy to digest using visualization tools or natural language output.
Grow into a Data-driven Company
It is more significant to understand data and business while organizations are rapidly adjusting to changes. Analytics has become more critical to doing everything from understanding sales trends, to segment customers, based on their online behaviors, and predicting how much inventory to hold to strategizing marketing campaigns. Analytics is what makes data a valuable asset.
Essential Capabilities of Augmented Analytics
Augmented analytics reduces the repetitive processes data analysts need to do every time they work with new datasets. It helps to decrease the time it takes to clean data through the ETL process. Augmented analytics allows more time to think about the data implications, discover patterns, auto-generated code, create visualizations, and propose recommendations from the insights it derives.
Augmented analytics considers intents and behaviors and turns them into contextual insights. It presents new directions to look at data and identify patterns and insights companies would have otherwise missed out on completely- thus altering the way analytics is used. The ability to highlight the most relevant hidden insights is a powerful capability.
Augmented analytics, for example, can help users manage the context at the explanatory process stage. It understands the values of data that are associated with or unrelated to that context, which results in powerful and relevant suggestions that are context-aware.
Modern self-service BI tools have a friendly user interface that enables business users with low to no technical skills to derive insights from data in real-time. In addition, these tools can easily handle large datasets from various sources in a quickly and competently.
The insights from augmented analytics tools can tell you what, why, and how something happened. In addition, it can reveal important insights, recommendations, and relationships between data points in real-time and present it to the user in the form of reports in conversational language.
Users can have data queries to get insights through the augmented analytics tools. For example, business users can ask, “How was the company’s performance last year?” or “What was the most profitable quarter of the year?” The systems provide in-depth explanations and recommendations around data insights, clearly understanding the “what” and the “why” of the data.
It enhances efficiency, decision-making, and collaboration between users and encourages data literacy and data democracy throughout an organization.
Augmented Analytics: What’s Next?
Augmented analytics is going to change the way people understand and examine data. It has become a necessity for businesses to survive. It will simplify and speed up the augmented data preparation, cleansing, and standardization of data, thus assist businesses to focus all their efforts on data analysis.
BI and analytics will become an immersive environment with integrations allowing users to interact with their data. New insights and data will be easier to access through various devices and interfaces like mobile phones, virtual assistants, or chatbots. In addition, it will help decision-making by notifying the users of alerts that need immediate attention. This will help businesses to stay updated about any changes happening in real-time.
Frequently Asked Questions
What are the benefits of augmented analytics?
Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs.
How important is augmented analytics?
Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors.
What are the examples of augmented analytics?
Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. These are some of the examples of the use of augmented analytics.
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Business Strategy
Article | April 21, 2022
When it comes to improving business performance, quite a bit of jargon gets thrown around. Corporate performance management (CPM), for example, is often used to refer to business performance management and enterprise performance management, but these terms don't always refer to the same thing.
CPM improves a company's capability. It helps the company enhance three fundamental values: performance monitoring, information delivery, and performance effectiveness. These values assist in understanding, improving, and managing the business. Within an integrated ecosystem, a corporate performance management system coordinates the performance of managers, employees, customers, and suppliers. Information access and strategic planning are the foundations of corporate performance management.
3 Reasons Why You Need Corporate Performance Management (CPM)
In the era of exploding business intelligence, businesses need to embrace process automation. CPM may profoundly impact your team's productivity, coherence, insight, and more.
CPM functions are critical to the C-suite and the long-term success of an organization. As a result, several businesses have developed departments solely dedicated to strategy and performance management. Let's look at the top reasons why you should use CPM for your business.
Addressing Challenges in Financial Data
Compiling your financial data takes time. To see and organize your financial data easily and quickly, you can use CPM software to connect with your ERP system. This application will also make the finance team's job simpler. It will be easier to understand and manage the projected estimates and how important they are.
Real-time Feedback
Smart dashboards in business performance management or CPM software provide every quantifiable statistic that a management team will need to use in its decision-making. Even though there are so many different types of data, it can be a good thing to read and use it as changes happen in real-time in the company.
Streamlined Reporting
Most businesses have several individuals involved in performance management, right from C-level executives to back-office administrators. Although not everyone is actively participating in the performance management process, many users need access to and analysis of reports.
CPM technology for a business focuses on a single source of information or data. That is why it provides greater control over it. It also gives more control and security over the results that come out of the process.
Who Uses CPM?
Earlier, CPM was primarily used by businesses with more than 1,000 workers. However, due to the affordability and simplicity of next-generation CPM solutions, dynamic and ambitious organizations from the startup phase to the enterprise level are now utilizing them. This is one of the prime reasons for the rapid increase in the CPM software market.
Companies that sense an opportunity to grow, large businesses that operate globally, organizations that merge with others, and businesses that strive to improve company performance are the most likely to use CPM.
Overcoming the Corporate Performance Management Challenges
When a business imparts great performance management throughout the workplace, critical expectations and desired outcomes must be set. Also, this does not always go as planned. As a result, CPM presents significant challenges that need immediate attention, as stated below.
Strategic Alignment
This involves ensuring that all organizational processes and essential components, such as finances, project and program management, risk management, etc., align with the primary goal.
Smart Automation
A poorly implemented CPM will result in complete failure. To make sure that information can be easily integrated, processed, and reported to meet specific standards, a company should build an ICT infrastructure that is easy to use, complete, and appropriate.
Synchronization of Objectives
Businesses should not depend only on current tactics while neglecting to develop their own. Instead, they should focus on getting their main objectives out in the open so that CPM and all stakeholders are on the same page.
Things to Consider While Choosing a CPM Platform
Before investing in corporate performance management software, understand your team’s requirements. What manual tasks do they currently execute? What tools will the team require to keep pace with the growth of the company?
Here we have mentioned the top three things to consider while choosing a CPM platform for business.
Usability
You want your employees to be passionate about the platform and its potential; choose an option that will significantly enhance their day-to-day functioning. Involve your team in the process of choosing a platform so they can give their opinion on how easy it is to use.
A user-friendly and accessible CPM system will lead to successful training, deployment, and an instant ROI.
Integrations with Existing Tools
Make sure your new CPM system can integrate with your existing systems. You may want to import data from your ERP system, BI tools, and spreadsheets in real-time to save time and effort by copying and pasting data between applications. Manual data re-entry takes a lot of time and puts data at risk of being missed or entered incorrectly.
This integration is crucial if your business utilizes a data warehouse to integrate data from multiple cloud tools. To do more analysis, you can also export data from the CPM platform into models and spreadsheets and presentations and word documents.
Value to the Organization
The cheapest option is not always the best one for your business. Evaluate the value of the software to the team, leadership, and organization. How can the program improve efficiency, offer accurate business visibility, and assist with data-driven decisions? This is where value is measured.
Conclusion
Corporate performance management is the framework that connects your organizational objectives with planning, enabling successful strategy implementation. Investing in CPM software will increase a company's efficiency by handling a reasonably straightforward process. When employees are free from tiresome activities, they can be employed for something useful that will contribute to the company's development.
“The true measure of the value of any business leader and manager is performance.”
– Brian Tracy
Frequently Asked Questions
What Is the Role of CPM in Business?
CPM software, which was earlier used in finance departments, is now meant to be used enterprise-wide, usually complementing business intelligence systems. There are graphical scorecards and dashboards for displaying corporate information in the CPM software. Forecasting, budgeting, and planning are some of the features that come with the software.
What Are the Primary Corporate Performance Metrics?
CPM is an aspect of business intelligence (BI) that includes monitoring and controlling a company's performance based on key performance indicators (KPIs) such as revenue, ROI, overhead, and operational expenses.
What Is the Difference Between CPM and EPM?
CPM concentrates on delivering a company-wide performance management solution, especially for the organization's finance department. EPM focuses on the overall performance of the organization, going beyond the finance departments to sales, marketing, supply chain, and other areas.
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