Unit4 and Softsensor.ai are on a voyage to reimagine FP&A for a Private Equity Company. All the data that is being minted by all the processes in any company is beyond the capability of excel. Any smallest private equity company would have at least a dozen of platforms they use to manage their different business process. Consolidating all the Data become challenging as this large data set goes beyond the capability of excel. Al-based Unit4 FP&A helps you mingle all this data digest it and help you visualize these data sets into meaningful insights.

How does all this benefit you as a CFO? You save time in consolidation, hence you can focus more on generating meaningful insights and transforming those insights into informed decisions.

What are the Downfalls?

B2B SAAS businesses scale a customer by adding more products to sell to the same customers or self-contained systems that are added opportunistically over time and the acquisitions of new companies with products. But as the businesses grow, the Quote to Cash [Q2C] process grows in complexity and becomes fragmented because of growth in
products, channels, and platforms. In these situations, the ERP and CRM platforms are in catch-up mode to the core applications, which are the essential revenue drivers.

B2B SAAS Organizations lose significant money in fragmented Q2C processes, leading to:

• Lack of customer revenue visibility, churn and upsell opportunity
• Increased challenges in billing, invoicing & collections
• A poor understanding of customer revenue,
• Lost upsell & cross-sell opportunities,
• Billing gaps,
• Failure to collect money on time,
• And gaps in cash applications

Over time, these challenges need untangling with a data-driven approach. Organizations can fix the processes by implementing and installing a new system which will force it to go through a complete & thorough review of the processes, and redesign. But this approach is expensive, may not always be easy to implement, and can take a long time.
A faster and quicker process fixing can be achieved by taking an analytical data-driven view of the key segments, outcome segments. This approach is more result-driven and can help uncover process bottlenecks, and challenges much faster, fixing them early in the life cycle.

What Makes It different?

Let us give you a full walkthrough of the new world of technology waiting to help you make operations faster, better, and cheaper!

Bottom-up decision tree view of segments

Building Contract & Order Visibility

Q2C visibility processes start with understanding two separate threads one of which begins in the salesforce or the front-end CRM and the other in billing & accounting software.

It is important to begin the journey at both ends for a quick understanding of the different lenses.

From salesforce:

It is important to extract the customer information, understand key customer id, contract information, and build trackable order data from the contracts.

This is a complex and complicated process. Most organizations have a mix of order types which may include measuring multiple delivery and action variables.

For ad-hoc purposes:

The organization needs to build this database, generate a taxonomy or method of segregating the contract types, and way to measure their contract obligations.

Before implementing any large-scale contract lifecycle management program, it is easier to find if there are other recordings of contract types, orders, and what needs to be billed.

It is important to build the revenue leakage data mart, understand the potential billing events and reconstruct if the organization is billing correctly all the billing events.

It is often a difficult and painstaking exercise and Al fools like NLP, RNN is useful in extracting document information and structuring them.

Billing visibility

A well-managed contract billing can help reduce disputes in debt collections. Writing and managing complex customer contracts can help organizations streamline collections by improving customer satisfaction, generating timely invoices, and improving visibility.

Each agreement specifies the billing terms, contacts, and limits, thereby significantly reducing the chances of disputes. An automated contract billing management system can help organizations solve these problems by:

• Improving productivity,
• Automating business processes,
• Easing complex billing scenarios,
• Supporting business management in understanding
• And analyzing billing events and increasing reporting accuracy.

Also, there are multiple people and at times multiple departments in a company responsible for B2B purchasing, which makes B2B billing a long and tedious process.

It is often a lengthy process of reviewing and approving the invoices before releasing the payment as the individual’s supporting payments are usually not the ones who initiate them.

The billing contracts need to be clear, and the ability to write and manage contracts is essential for proper AR management.

Product usage analysis:

It refers to monitoring how the customers are interacting with the product, the frequency of usage, and why they are doing what they are doing. Not only does it help improve user experience by helping track the product data, but it also indicates products might need more attention during debt collection.

The products that are used frequently would be required again by the customer, and the collection would be easier, but the products that are not being used frequently might be identified as high-risk products, as the customer might discontinue purchasing, which may result in delayed payments.

Tracking Payments

As organizations grow, the growth in the number of customers making payments with multiple payment channels grows. Different customers prefer different payment methods ranging from cash, checks, cards, wire transfers to payment gateways.

To keep a proper track of the payments received, data influx from multiple sources needs to be visualized, lack of which leads to the payment visualization and understanding going down.

An increase in the number of products also accounts for growing billing complexity, with each product impacting the billing procedure.
One customer might pay for one product but delay the payment for another product for a long time, which affects the payment cycles. Such long billing and payment cycles make the billing systems even more challenging.

Managing the AR invoices enhances cash flow and helps debt collection by reducing non-payments. Maintaining a system for sending out invoices is essential for reducing the AR risk.

An automated system that notifies the customers before sending an invoice keeps track of the payments, sends late invoice notices, transfers the overdue invoices to the collection team can help ease the collection process.

It also provides the organization with the ability to present their customers with precise, well-managed Al-enabled invoice matching that offers better visibility, builds trust, and speeds up the collection process.

Tracking Delays and DSOs

1. Analytics Keep You a Step Ahead:

The very first requirement of an effectively managed collections system is proper and timely billing. The invoices generated must be accurate, complete, provide all the required information, must be processed, and sent timely and to the right person to avoid payments from being deferred or neglected.

Automated billing, accurate visualization and tracking, proper visibility to the client, timely follow-ups, and the use of data to analyze and predict bad debts can lead to faster collections and improved cash flow.

2. Improving the visibility of the process

Understanding payment processes to track and review payments can:

• Improve collection and eliminate disputes.
• It can help track delayed payments and identify if it gets regular with a specific customer.
• Help avoid false rejections by identifying trends
• Reduce fraudulent transactions by training a detection model.
• Provides real-time insights that help make critical business decisions,
• Offers end-to-end information on the expenses made on payment processing,
• Provides a complete view of the model, route, provider, and currency of the payment made.

3. Predictive analytics

Predictive analytics can help reduce overwhelming debt considerably:

• Before collections, by providing an analysis of prior payments;
• During collections, for prioritizing customers and customizing settlements;
• After collections monitor the products that need attention.

Identify high-risk accounts and forecast the effective treatment and payment methods for each account. It takes a combination of digitization, analytics, and technology to run a B2B collection process smoothly.

Multiple systems that automate the contract billing, invoicing, usage monitoring, invoice matching, AR tracking. It can be integrated with various systems for a clear view of the received and the pending payments and to help manage the debt.

There is a long way for analytics and Al solutions like chatbots and virtual assistants to transform the way collections are managed and cash flows are improved.

Nevertheless, the exponential growth of A.l and Analytics in transforming every industry has surely made us say “Future is Now”

So, are you ready to leverage the power of A.| and Analytics to take your business to the next level?

Leveraging the power of analytics and Al for debt collection,
Get in touch with the industry experts!

COMPANY PRESENTATION

How can we help you?

We have helped clients across Asia, Europe, and Americas to transform their challenges into impactful improvements.

The post How predictive analytics and automation can help you transform collections appeared first on softsensor.

In today’s dynamic financial landscape, organizations are on a relentless quest to enhance their financial forecasting and budgeting processes. Traditional methods, characterized by manual workflows and error-prone data handling, are becoming obsolete in the era of data-driven decision-making. Enter Artificial Intelligence (AI), a transformative force revolutionizing financial planning. In this blog, we’ll uncover the challenges associated with traditional methods and explore how AI is reshaping financial forecasting and budgeting. 

Challenges of Traditional Financial Forecasting and Budgeting 

Before we embark on the AI revolution, it’s crucial to grasp the limitations of traditional financial forecasting and budgeting methods: 

• Excel Limitations: While Excel remains a valuable tool, over-reliance on it for heavy data management can lead to discrepancies and potential security risks in modern organizational contexts. 
• Data Silos: Data is frequently scattered across various departments, making consolidation and effective analysis a daunting task. The lack of data integration hampers comprehensive financial planning. 
• Lack of Real-Time Insights: Traditional methods primarily provide historical data, lacking the real-time insights necessary for proactive decision-making. In today’s rapidly evolving business environment, this lag in data can be detrimental. 
• Limited Scalability: As organizations grow, traditional methods struggle to handle increasing data volumes and complexity. This scalability issue hampers flexibility and adaptability. 
• Inefficient Scenario Planning: Preparing for various scenarios and contingencies is cumbersome and often inadequate with traditional approaches. This leaves organizations ill-prepared for unexpected changes. 

Five Ways AI is Revolutionizing Financial Forecasting and Budgeting 

Let’s explore how AI addresses these challenges and transforms financial forecasting and budgeting: 

• Enhanced Accuracy: 

AI’s most significant advantage is its ability to enhance accuracy. Unlike manual methods susceptible to human errors and biases, AI-driven forecasts rely on robust statistical models. AI analyzes vast datasets and recognizes complex patterns, resulting in more reliable financial planning. This newfound accuracy allows organizations to make critical financial decisions with confidence, reducing the risk of costly mistakes and enhancing overall financial stability. 

• Real-Time Insights: 

Timeliness is crucial in finance, and AI excels in providing real-time insights. While traditional methods rely on historical data and manual processes, AI continuously processes and analyzes incoming data streams. This offers instantaneous insights into financial trends and developments, empowering organizations to make data-driven decisions promptly, a game-changer.  

• Efficient Scenario Analysis: 

AI streamlines scenario analysis, a crucial aspect of financial planning. With its computational power, AI can effortlessly generate and assess multiple scenarios simultaneously. Organizations can model a range of economic conditions, from best-case to worst-case, and everything in between. This newfound efficiency in scenario planning enables organizations to be well-prepared for economic downturns, market fluctuations, or unexpected disruptions. AI’s scenario analysis helps develop robust contingency plans and make informed decisions that factor in various potential outcomes. 

• Cost Optimization: 

Cost optimization is paramount in today’s competitive landscape. AI excels in identifying cost-saving opportunities that might otherwise remain hidden. By analyzing historical financial data, expense patterns, and market conditions, AI systems pinpoint areas for cost reduction. For instance, AI may identify inefficiencies in supply chain management, suggest optimization strategies, or highlight opportunities for vendor negotiation. Improved cost optimization not only boosts profitability but also enhances financial resilience during economic uncertainties. 

• Strategic Planning: 

AI-generated insights free up finance professionals from routine data processing, allowing them to focus on strategic planning. AI provides accurate forecasts, real-time insights, and scenario analysis, supporting strategic thinking. Organizations can align their financial strategies with broader business objectives, ensuring long-term financial sustainability. This strategic vision is essential for thriving in a dynamic and ever-changing financial landscape.

The post 5 Ways AI is Revolutionizing Financial Forecasting and Budgeting appeared first on softsensor.

In the ever-changing landscape of NPOs (Non-Profit Organizations), the sector is going through a transformation far beyond how finances are managed. This shift involves a comprehensive approach, integrating innovative strategies to boost operational efficiencies, enhance donor management and maximize program effectiveness and at the heart of this transformation lies FP&A (Financial Planning and Analysis). 

FP&A plays a crucial role in the NPO sector involving meticulous collection, organization and analysis of financial data. It provides them with a powerful toolkit, enabling them to make informed decisions, resource utilization and enhance operations. 

This progressive leap in financial strategy isn’t all about spreadsheets and numbers, but overall reshaping of the very fabric of NPOs themselves. By embracing FP&A methods they challenge each distinctive test in a different method ensuring that they achieve superior outcomes and efficiency in day-to-day operations. 

Streamlined FP&A isn’t just a trend but a game changer. By providing NPOs with strategic insights that they need to thrive in this ever-changing landscape. As they keep adapting to modern methods it enables them to make a lasting impact on the communities that they serve. 

Addressing FP&A Challenges in Nonprofit Organizations    

From managing donor contributions to adhering to stringent regulatory standards – challenges for nonprofit organizations are diverse and complex, especially when it comes to managing finances.  

• Excel Limitations: While Excel remains a valuable tool, over-reliance on it for heavy data management can lead to discrepancies and potential security risks in modern organizational contexts. 
• Donor Transparency: Providing clear, accountable reporting on donations is an ongoing challenge. 

• Impact Measurement: Quantifying the impact of programs is essential for credibility but can be challenging. 
• Effective Budget Allocation and accurate forecasting: Allocating resources efficiently among numerous programs and forecasting requires careful planning. 

Embracing Modern Technology in Nonprofit Financial Management 

Today there’s a significant shift towards embracing modern technological solutions. Where once financial planning and analysis (FP&A) in nonprofits depended heavily on manual processes, such as hand-entered data and spreadsheets, or storing documents in physical files, today’s tech advancements are changing the game. These modern tools bring streamlined data management, the ability to analyze data in real-time, and improved collaborative capabilities. Cloud computing, for instance, offers a secure and easily accessible way to store data, moving away from the vulnerabilities of physical document storage. Additionally, the integration of artificial intelligence (AI) and automated processes is transforming how data is handled, freeing up finance teams to concentrate more on strategic planning and decision-making. This technological shift is crucial for nonprofits aiming to enhance efficiency and effectiveness in their financial operations. 

Streamlining Nonprofit Financial Management with Unit4 FP&A and Softsensor.ai 

What is Unit4 FP&A tool? 

Unit4 FP&A tool is specifically designed to enhance financial planning and analysis for organizations, that immensely benefits wide organizations like nonprofits. Its features include: 

• Automated Customized Budgeting: Simplifies the budgeting process, minimizing manual inputs and errors. 
• Advanced Scenario Planning: Unleashes the power of ‘what-if’ analysis and re-forecasting with robust version control. 
• In-depth Financial Analysis: Offers detailed insight into financial data across various dimensions like project and activity. 
• Dynamic Financial Adjustment: Facilitates flexible financial management with multi-level value adjustments. 
• Improved Donor Management: Provides comprehensive views for better donor finance management and transparency. 
• Efficient KPI Tracking: Supports the effective tracking of key performance indicators, ensuring accurate program impact measurement. 
• Role-based Access Management: Guarantees data security with access tailored to specific roles. 

Softsensor’s Collaboration with Unit4 – A FP&A Transformation 

Softsensor.ai employs a phased, four-step approach over a brief period for Unit4 FP&A standard implementation. Initially, we focus on a comprehensive pre-implementation assessment. Subsequently, we tailor the system to the NPO’s specific needs. The final phase involves extensive knowledge transfer and training. Following the initial implementation period, Softsensor.ai continues to supply ongoing support and improvement services. 

We provide: 

• Tailored Implementation: Starting with a comprehensive pre-implementation assessment, we ensure the system configuration aligns with your NPO’s unique needs. 

• Knowledge Transfer: We offer in-depth training, equipping teams to effectively use FP&A software. 
• Continuous Support: Our ongoing guidance is designed to optimize performance and tackle emerging challenges. 

Transformative Outcomes with Softsensor.ai and Unit4  

• Streamlined Budgeting: Reducing budget cycle preparation time by up to 60%. 
• Data Preparation Efficiency: Achieving an 80% reduction in data preparation efforts. 
• Enhanced Accuracy: Significantly reducing errors in financial reporting and forecasting. 
• Improved Forecast Precision: Offering more accurate financial forecasts. 
• Real-Time Reporting: Facilitating access to and analysis of financial reports instantaneously. 
• Increased Team Productivity: Boosting finance team efficiency by up to 80%. 

Begin Your Transformation Experience the transformative power of Unit4 FP&A with a demo. See firsthand how your NPO can enhance efficiency, transparency, and impact. Book Your FP&A Demo Today – Start revolutionizing your nonprofit’s financial strategy.

The post A Dynamic Duo for Nonprofit Financial Success appeared first on softsensor.

In 2020, 54% of enterprises agreed that cloud-based Bl was vital to their current and future initiatives. More than 46% of businesses already use a Bl tool as a core part of their business strategy. So, what makes Bl Strategy a critical ingredient in the recipe of success.

The Bl market will hit $33+ billion by 2025. With the onset of COVID-19 and cloud-based solutions, we have witnessed a seismic shift in the work culture and incorporation of technology into the business strategies and business frames.

Today, most businesses are desperately searching for ways to stay competitive in the age of big data. One of the best ways to do this is business intelligence (Bl). And while it is tempting to approach Bl with a “throwing money at a problem” mentality, this is not the best use of resources. Instead, a well-considered and targeted Bl strategy is the key to unlocking the value lurking in your data.

As businesses have realized the value of business intelligence, they also need to invest in it. But what is the best way to approach the Bl strategy? Should you hire a consultant to design the perfect implementation, or should you take a more hands-on approach? How do you know which tools and technologies to use, and how do you put together a cohesive business case for investment?

Building a successful Business Intelligence (Bl) strategy requires more than just developing a robust data analytics capability. It requires

• understanding the goals of the analytics,
• the role of the data in those goals,
• and the priorities in place to ensure that the correct data is being used in the right way.

You cannot simply build a Bl strategy and then expect it to work; you must build it with the intent of achieving specific, measurable goals.
Let us define Business Intelligence [BI] from scratch:

What is Bl Strategy?

The Bl strategy is the roadmap for achieving your business goals within your organization. It is the roadmap for mapping out how you will capture, store, and analyze data, defining how you will connect to data sources and load and transform data. Most importantly, the Bl strategy defines how you will measure the success of your Bl initiatives to determine whether the investments you are making are the correct ones.

A Bl strategy enables your organization to achieve strategic goals using business analytics. The strategy should be the logical culmination of all the steps taken in the process of the data discovery lifecycle.

The strategy should identify the goals you want to achieve using your business analytics solution and the data requirements to achieve those goals. It should also identify the key performance indicators (KPIs) that will help you measure whether your solution achieves the goals defined in your Bl strategy. It sets the direction for your Bl investment, the vision for how you will use data to improve your organization, and it defines the objectives you will use to measure the success of that strategy.

Your Bl strategy should be SMART:

• Specific,
• Measurable,
• Attainable,
• Relevant,
• Timeframe.

Bl Strategy involves:

• Determining the goals, you want to achieve through your Bl solution
• Designing a solution that maximizes the value of your data
• Defining the structure of the Bl team, the roles, and responsibilities of the team members,
• the metrics by which you will measure the success of the strategy.
• Defining the scope of the Bl team,
• and the budget that will be allocated for the team.

What makes the Bl Strategy one of the critical elements in making data-driven intelligent decisions in the opulence of data floating around ?

Bl is a strategic investment. It is a long-term investment and will take time to implement correctly.

Building a Bl strategy is both theory and practice driven. The theory is driven by our understanding of business, challenges, and needs, and the practice is driven by your product, data, and operational maturity. The theory and practice are in constant flux as your Bl strategy evolves.

A powerful Bl strategy will provide the foundation for decision-making and continuously improving data usage, right from selecting the right data warehouse, data source, and the correct analysis and visualization solution. It involves

• aligning your data with the analytics and business needs of your organization,
• enabling better data governance,
• enabling better data analysis and better data enrichment,
• providing better data discovery and enabling better data integration and transformation.

A Bl strategy is also valuable for setting the direction of your organization. Let us get on board with 8 steps for creating a successful Bl strategy.

Step 1: Define the Problem You Want to Solve with BI

Before you can build a successful Bl strategy, you first must define the problem you are trying to solve. Most businesses end up building a Bl strategy without having a clear idea of why they need it in the first place. It is essential to look beyond the obvious to unearth the valid reason your business needs to gather information from its data. Is your data lacking certain vital pieces?

It is also essential to start with an actionable goal. What do you want to achieve if you are trying to determine why your business needs BI? Your goal should be defined in a clear, concise, and unambiguous way. It should also be measurable.

Step 2: Get Input from Stakeholders for the Bl Strategy

Once you have a clear idea of the data needed to achieve your goals, the next step is to get the input of your stakeholders. Your stakeholders will have a lot of valuable insight into your organization’s data needs, so it is essential to get their input.

The stakeholders you identify will include the data owners, data analysts, data users, business analysts, and others affected by your Bl strategy. It is essential to get a wide variety of input from all these individuals because their input will help you determine the
data requirements for achieving your goals.

You will need to identify the key decision-makers for your analytics strategies, such as the CEO, COO, and CFO, and get their input on your plan for data acquisition. You will also need to identify the other critical stakeholders in your organization, such as the data scientists, business analysts, and data engineers, and get their input on your plan for data acquisition. This step is critical because it will help you understand the needs and priorities of the people using your Bl solution.

Step 3: Create a Plan for Data Acquisition

One of the most critical aspects of a successful Bl strategy is data acquisition. There is no way to build a warehouse, data model, or visualize it without data. Data acquisition refers to gathering data from various sources and putting it into a format that can be used in a Bl solution. The success of your data acquisition strategy will determine the success of your Bl strategy.

The first step in creating a plan for acquisition is to identify your key sources. You will need to know the specific data that your organization generates, such as sales, product, marketing, customers, revenue data, etc.

One of the most common mistakes made by Bl analysts is collecting too much information. A successful acquisition plan is to identify the necessary data items for achieving your goals and then acquire only the necessary to achieve your goals.
You do not want to end up with a lot of data collecting dust, or worse, gathering digital dustbin. It is essential to define what is needed to achieve your goals and acquire only the required.

Step 4: Compare vendors

Once you have a plan for data acquisition, the next step is to identify the right vendors for achieving your goals. This step is often the most difficult for Bl analysts because it requires you to step away from your analytics solution and evaluate it objectively. This is a complex decision, and there are many factors to consider.

The first factor to consider when evaluating vendors is the solution’s price. You will want to find a vendor who offers a solution at an affordable price for your budget.

You will need to identify several factors when comparing vendors, such as the data architecture, data governance, data quality, data security, and solution cost. It is essential to be objective when comparing vendors because a Bl solution that is perfect for your organization may not be the best solution for every organization.

Step 5: Choose the Right Tools for the Job

Part of a comprehensive Bl strategy involves choosing the right tools for your job. The right tools are not only essential for your data analytics job but for everything you do with Bl. They are also an exceptional way to add value to your business.
The right tools for the job will make your Bl efforts easier; the better they work, the more likely you will achieve your goals. The right tools can be classified into two distinct categories: technical tools (such as Bl tools) and non-technical tools (such as analytic tools).

Ensure that the Bl tool you choose is both flexible and robust to implement your Bl strategy quickly and effectively.

Step 6: Develop an implementation plan

Once you have identified the right vendor for your analytics solution, the next step is to develop an implementation plan. Each organization is unique, and each business needs a customized solution for achieving its goals, so it is essential to tailor your implementation plan to your organization.
You will want to identify the critical components of your analytics solution, such as the data architecture, data governance, data quality, data security, and the platform. You will also want to identify the stages in the data lifecycle for achieving your goals and the data requirements for each stage.
This involves determining

Step 7: Determine the Data Warehouse Design

Once you have identified the appropriate number of data collectors, the next step is to determine the data warehouse design. This involves determining

• the data warehouse architecture,
• the schema and the storage engine,
• and the data warehouse technology.
• Identify the appropriate number of data sources
• and the number of data points you will collect.
• Determine the number of intervals to collect data
• and the number of data points each time.
• Identify the stages in the data lifecycle for achieving your goals and the data requirements for each stage.
• identify the key performance indicators (KPIs) that will help you to determine whether your Bl solution is. The data warehouse is the central repository for your organization’s data. It is where all your data is stored, loaded, and analyzed, and it is where all the reports go.

It is the foundation on which all other data exploration and analytics are built. It is where data scientists and business analysts perform their analyses and where IT administrators and system administrators perform their day-to-day tasks.

Step 8: Determine the Scaling Strategy

Once you have an implementation plan in place, the next step is to determine the scaling strategy. This involves determining how you will scale your analytics solution over time and identifying the critical components of your scaling strategy.

You will want to identify the stages in the data lifecycle for achieving your goals and the data requirements for each stage. You will also want to identify the key performance indicators (KPIs) that will help you determine whether your Bl solution meets your organization’s needs.

This involves determining the appropriate number of users for achieving your goals and then scaling your solution based on the number of users. You will also need to determine the appropriate number of data sources for achieving your goals and scale your solution based on the number of data sources.

Democratize data, empower the user, and drive innovation.

The key to unlocking the value of data within your organization is to democratize data. It involves breaking down the walls that separate data silos, so that data can be used and analyzed by all the users within your organization.

This empowers your users to make better decisions, enabling them to drive innovation by discovering new patterns, relationships, and insights within your organization’s data. Data democratization also empowers your organization to unlock the value of data by turning data into a competitive advantage.

As you have determined the critical elements of your Bl strategy, it is crucial to evaluate them. Consider the potential benefits and challenges you will face when implementing your Bl strategy.

Connecting the Data Points:

With 8 in 10 organizations that use Bl for analytics having seen it work successfully, imagine a comprehensive partner that guides, empowers, and brings you the best of the world on the journey of Bl Strategy. Softsensor Al is committed to intelligent and customized solutions that deliver value.

We believe with the right tools and years of unmatched expertise by your side. You can experience the revolution of positive change in every sector of your organization. Equip yourself for the future with us!

The post 8 Steps for Creating a Successful Bl Strategy appeared first on softsensor.