In the last decades, the corporate market bought a promise: the more software licences are acquired, the more modern and efficient it will be. However, the practical result of this technological race is the emergence of a hidden and highly harmful phenomenon to finances and business agility: data fragmentation.
Today, a medium or large size company commonly uses dozens of different tools. There’s a system to manage customer relationships, another to issue invoices and control cash flow, platforms to control market campaigns and many sheets created with the purpose of “plug the hole” left by the software.
The biggest problem isn’t the quantity of tools used, but the fact they operate in isolation. When data doesn’t communicate with each other, leadership loses sight and decisions become based on feeling and not on structured data.
If your company wants to escalate predictability and sustainability, it’s fundamental to overcome chaos of unconnected systems. In this article, we will analyse the biggest financial impact of this fragmentation and show how data engineering allied with artificial intelligence can unify your operation.
How to identify data fragmentation on your company
Data fragmentation doesn’t usually manifest as a critical error that paralyzes the company overnight. It acts as a constant drain on energy, time and money.
There are clear signals that your operation is suffering from the systems being unconnected. Identifying these symptoms is the first step to stop the damage:
1. Board meeting with divergent numbers
A marketing manager shows up an report indicating that 10,000 qualified leads were generated. The sales manager, in turn, states that the team only received half of these contacts on the CRM, and the financial team reports a billing that doesn’t add up with the registered sales recorded by either of these areas.
When the systems aren’t integrated to a single source of truth, each sector creates it’s own metric and interpretation of reality. It results in more time wasted discussing which data is correct than designing strategies to make the business grow.
2. Spreadsheets created to “plug the hole” between systems
Company members spend hours exporting CSV files from sales systems, importing on the financial system and crossing data manually with complex formulas. These sheets, many times managed by only one person, turn vital to the company.
If the spreadsheet creator leaves the company, or if a single line is deleted by mistake, all of the operational visibility is lost. This is not a healthy ecosystem and the damage caused by this can make noises that fragment the data even more.
3. Making decisions looking back
If your team needs two weeks of manual processing to know the real profit of a product or the client’s cost of acquisition of the current month, it means that you are managing the company looking back. When the report reaches the board of directors, the stated problem already happened and the sales opportunity already passed by.
The invisible cost of isolated information
The lack of business system integration is not only a bureaucratic inconvenience, it generates indirect and direct costs that drain the profit margin. We can subdivide this impact in three major slopes:
- Cost of opportunity: qualified professionals, hired to analyse strategies, negotiate contracts and optimize processes, spend most of their time mining data, cleaning and constructing operational reports. This represents a massive waste of intellectual capital.
- Operational errors: when data is filled manually in a tax receipt, a contract or a purchase order, humans can make mistakes, resulting in tax penalties, incorrect delivery and direct financial losses.
- Friction in customer journey: if the support team doesn’t have fast access to sales history or the interactions with the client on the marketing channel, the customer is needed to repeat itself every time they change the attention channel.
The path of solution: data integration and intelligence engineering
The best way to implement a definitive solution, is to maintain the tools already used and build a layer of intelligence and structured integration above them. This approach is based on creating a fluid data ecosystem, where information runs in real time through all the veins of the organization.
Step 1: Capture and centralization
Instead of allowing each software to keep its data in closed databases, automated data extraction flows are created. With secure integrations, sales, financial, customer service and marketing information are collected continually directed to a centralized repository, projected to sustain a large volume of data.
Step 2: Data cleaning and harmonization
Raw data from different sources are often confusing. Marketing can register a client using “Email”, while the sales team registers by its Employer Identification Number. This is the stage where the data are cleaned, standardized and cross-referenced. The artificial intelligence works here to identify duplicities, correct textual inconsistencies and categorize information.
Step 3: Democratization of access
With clean and unified data, the time comes to put them in use. It translate on creation of customized dashboards to each level of decision:
- To directors: macro vision of business health;
- To managers: indicators of operational bottlenecks, as response time or conversion rate;
- To operation: prioritized task list based on urgency data and client value.
The role of AI in the unified data era
Applicating AI models on an integrated and unified database, technology begins to act as a strategic advisor to operation:
Predictive churn analysis
AI can analyse historical behavior crossing data from support, product usage and financial interactions. When it detects a pattern of behavior that typically leads to cancellation, the system emits an alert to the Customer Success team to take preventive action.
Demand and inventory predictability
While crossing sales history data, market seasonality and campaign performance, AI can predict the demand volume for the coming months. This allows the purchase and logistics department to optimize inventory, avoiding both exceeding idle capital and stockouts.
Natural language queries
Managers and company members can use an internal AI to access data without needing a BI analyst to generate new reports. With a unified database, AI can answer questions regarding any company queries using natural language.
Practical comparison: before and after of intelligent integration
This table represents the comparative of common process before and after of centralizing the data:
Conclusion
Data fragmentation is a hidden wall that prevents promising companies from reaching its desirable scaling potential. Continuing operating based on manual reports and systems that don't communicate with each other is to accept the inefficiency and open space for more agile competitors to dominate the market.
The system integration engineering allied to artificial intelligence came to democratize the access of strategic information. It transforms dispersed data in operational clarity, allowing leadership to govern the business with mathematical precision and enabling the operation team to focus on generating value and delighting the customer.
