Data Orchestration: 7 Signs You Need It In Your Business

In the world of business, data is the fuel that powers innovation and drives decision-making.

Yet, as the volume of data grows exponentially, it becomes increasingly challenging to extract insights that deliver real business value.

No wonder, merely 24.4% of companies have embraced a truly data-driven culture within their organizations (New Vantage Partners: Big Data and AI executive Survey, 2021)

This is where data orchestration comes into play – a strategic approach that enables organizations to align and optimize their data ecosystem, unlocking the true potential of their data universe.

Let’s make it simple – What is Data Orchestration?

Data orchestration is the art of harmonizing disparate data sources, systems, and processes, providing a unified view of an organization’s data landscape. It is the conductor that synchronizes the different parts of a complex data universe, creating a masterpiece of insights that enables organizations to make informed decisions, drive innovation, and achieve their business objectives.

7 Signs You Need Data Orchestration in your Organization

1. Data Silos:

Data silos occur when different departments or teams within an organization maintain their own separate databases or systems that don’t effectively communicate or share data. This can lead to several issues, such as:

• Lack of visibility: Data is scattered across various systems, making it difficult to get a holistic view of the organization’s data assets.

Decision-makers may lack comprehensive insights and struggle to make informed decisions.

• Inefficient collaboration: Teams may work in isolation, unable to access and share data seamlessly.

Collaboration becomes challenging, leading to duplicated efforts, miscommunication, and reduced productivity.

• Inconsistent data: Different systems may use different data formats, structures, or definitions, leading to data inconsistencies and difficulties in data reconciliation and aggregation.

2. Manual Data Processes:
   Manually entering data has its own disadvantages:

• Time-consuming: Manual data processes, such as manually extracting data from multiple sources, transforming it, and loading it into different systems, are labor-intensive and time-consuming. This slows down data-related operations and hampers agility.
  
• Error-prone: Manual data processes are susceptible to human errors, including data entry mistakes, formula errors, and data inconsistencies. These errors can propagate throughout the organization and compromise the accuracy and reliability of data-driven insights.

• Lack of scalability: As data volumes grow, manual processes become increasingly challenging to scale. It becomes difficult to handle large data sets, process data in real-time, and meet evolving business needs efficiently.

.

3. Lack of Data Consistency and Quality:
   
   Inconsistent or poor-quality data can significantly impact decision-making and analysis. Signs of data inconsistency and poor quality include:

• Data discrepancies: When the same data element exists in different systems or sources but contains different values or definitions, it leads to data discrepancies. This makes it challenging to reconcile and trust data across systems.

• Data duplications: Duplicate records or entries can occur when data is not properly consolidated or integrated. Duplicates can skew analysis results, lead to inaccurate reporting, and waste storage resources.

• Missing or incomplete data: Incomplete or missing data can undermine the reliability and comprehensiveness of analysis. Decision-makers may lack essential data points, hindering their ability to derive accurate insights.

4. Data Integration Challenges:
   
   Integrating data from different sources can become complex and time-consuming, indicating a need for data orchestration. Common challenges include:

• Heterogeneous data sources: Organizations often deal with diverse data sources, including databases, applications, external APIs, and cloud services. Each source may have its own data formats, protocols, or access methods, making integration challenging.

• Data latency: Timely data integration is crucial for real-time or near-real-time analytics. Delays in data integration can result in outdated insights, reducing the organization’s ability to respond quickly to changing circumstances.

• Complex integration requirements: Integration may involve data mapping, data transformation, data enrichment, or handling complex data relationships. The complexity increases as the number of data sources and integration requirements grow.

• Data reconciliation: When data from multiple sources needs to be merged, reconciling and aligning the data becomes complex. Inconsistent data structures or data semantics can hinder the integration process.

• Data volume and velocity: As data volumes and the speed at which data is generated increase, traditional manual integration approaches may not scale. Handling large data volumes and processing data in real-time or near real-time becomes challenging.

5.  Inefficient Data Analysis and Reporting:
   
   If it takes a significant amount of time to gather, cleanse, and prepare data for analysis or reporting purposes, it may indicate a need for data orchestration. Common issues include:

• Data preparation bottlenecks: Manually preparing data for analysis, including data cleansing, transformation, and consolidation, can be time-consuming and error-prone. It delays the availability of insights and reduces agility.

• Inconsistent reporting: In the absence of standardized data and reporting practices, different teams or departments may produce inconsistent reports, leading to confusion and conflicting information.

• Limited self-service analytics: Without data orchestration, accessing and analyzing data often requires technical expertise, creating a dependency on IT or data teams. This hampers self-service analytics and delays decision-making.

6. Compliance and Security Risks:

Organizations dealing with sensitive data or operating in regulated industries face compliance and security challenges. Signs that indicate the need for data orchestration in this context include:

• Data privacy concerns: If data privacy regulations, such as GDPR or HIPAA, are applicable to your organization, managing and protecting sensitive data becomes critical. Inadequate data management practices may expose the organization to compliance risks and legal penalties.

• Lack of data governance: In the absence of proper data governance frameworks, organizations may struggle to ensure data security, access controls, and auditability. This can compromise the integrity and confidentiality of data.

• Fragmented security practices: When data is scattered across different systems and sources, implementing consistent security measures becomes challenging. It can result in vulnerabilities, unauthorized access, and data breaches.

7. Inability to Leverage the Full Potential of Data:
   
   If your organization struggles to derive meaningful insights or unlock the full potential of your data assets, it may indicate a need for data orchestration. Common challenges include:

• Lack of data-driven decision-making: If data is underutilized in decision-making processes, relying more on intuition or anecdotal evidence, it suggests a missed opportunity to leverage valuable insights.

• Inability to extract actionable insights: Despite having access to a large volume of data, if the organization struggles to extract actionable insights that drive business outcomes, it indicates a gap in the data analysis process.

• Limited data-driven innovation: If the organization is not harnessing data to identify new opportunities, drive innovation, or optimize business processes, it may lag behind competitors who leverage data effectively.

Data Orchestration and Data Integration

Data orchestration and data integration – a dynamic duo that unlocks transformative possibilities.

Data integration serves as the bridge, seamlessly merging disparate data sources into a unified whole. It breaks down silos, reveals hidden insights, and amplifies decision-making.

Together, data orchestration and data integration create a synergistic symphony. They teach us that by harmonizing our data, we can drive unparalleled success in today’s business landscape. 

Embrace this powerful partnership and integrate your ERP and CRM with Commercient SYNC. Let your data harmonize, orchestrate a symphony of insights, and propel your organization toward new heights of achievement. The symphony awaits your conductor’s baton. Talk to us today.