Data Integration Challenges And How To Overcome Them

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Data Integration Challenges and How to Overcome
Them
The efficient functioning of the sales team
purely depends on the real-time deployment of
quality data. Have you ever been to a car
dealership? You must have come across cases,
where a specific car model is unavailable, so
the sales representative checks up in real- time
to see whether its available at their other
dealership. The sales representative can do this
due to the smooth process of data integration
which happens companywide. Data Integration
Explained The evolution of tech has enabled
companies to gather an immense amount of data
consistently from multiple sources. If you have a
global business, the data involved would be
similar to a confetti bomb. Sorting out relevant
data for use would be slow and require a great
deal of manual work. Here is where data
integration comes into play. The data
integration process is employed efficiently, to
assemble and catalog data from multiple sources.
Fortune Business
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• Insights projects the global data integration and
  integrity software market to grow to 30.21
  billion.
• The Impact of Data Integration on Sales
• As per a combined study by Forbes and McKinsey,
  organizations relying on data are 23 times more
  likely to acquire customers, and 6 times more
  likely to retain customers. Data integration
  merges all available data and makes it easier
  for the sales team to access data in real time
  and across all verticals. You can use data
  integration to manage consumer data, their
  current stage in the sales funnel, purchase
  intent, previous purchases, and more. Through
  this unified data, you can easily chart out a
  sales strategy to target existing consumers and
  even convert leads into customers.
• The Challenges
• A bulk of companies have become data-driven, so
  integrating multiple data points is a must.
  While data integration is a potent process, it
  also comes with a lot of challenges.
• Diverse Data Formats
• As data integration gathers data from multiple
  sources, the data formats are liable to be
  different. Suppose you have a sales team in the
  US and another in India the sales team in the US
  will enter the date in mm/dd/yyyy format, while
  the sales team in India will enter the date in
  dd/mm/yyyy format. The different data formats
  will make the data comparison between both teams
  difficult.

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• Outdated and Low-Quality Data
• Without set data entry and maintenance standards,
  data integration combines all forms of data into
  a single accessible format. As a result, the
  data sets would include outdated and decrepit
  data. Data duplication is also a likelihood with
  multiple teams inputting the same data. The odds
  of this happening increase when companies dont
  organize their databases regularly.
• Using The Wrong Integration Software
• Multiple data integration software exist in the
  market that cater to a specific set of data
  requirements. Using the wrong data integration
  software can lead to a potential data mix-up with
  a chance of data corruption. It might also lead
  to inefficient and inaccurate data collection.
• Excess Data
• If you have not set parameters for collecting
  data, there are chances that you might be
  collecting all inflowing data. It could lead to
  data bloating, and crucial data could get buried
  under scores of worthless data. The problem gets
  compounded when you gather data from a diverse
  range of sources, which could make it a colossal
  challenge to analyze for valuable data.
• The Solution
• Automate Data Pruning

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• Way before AI came into play, many processes were
  still automated. After AI, the automation
  processes became a lot more efficient and
  error-free. You can utilize AI to automate data
  pruning during the data integration process. AI
  can easily detect duplicate and insignificant
  data, and prune it during the integration
  process. The automation process will lead to
  data integration that is accurate, efficient,
  and easier to analyze.
• Utilize and Scale Data Management
• Setting data management guidelines can help you
  better manage diverse data formats gathered from
  varied data collection sources. Data management
  can efficiently scour through all integrated
  data. As a result, you can efficiently make
  accurate, data-driven decisions. Scalability is
  also a significant aspect of data management.
  Data management is a continuous process, and as
  your company grows, data management also needs
  to be scalable enough to manage an influx in
  data integration.
• Choose the Correct Integration Software.
• A smooth and efficient data integration process
  requires the correct integration software. Data
  integration software falls under five different
  types
• Manual Data Integration
• Data Integration with Middleware

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• Uniform Access Data Integration
• Application-Based Data Integration
• Common Storage Data Integration
• You must answer three key questions to choose the
  correct data integration software
• Q1. Which data are you looking to integrate?
• Q2. Do you require manual data pushes or
  automated synchronized data pushes?
• Q3. Do you want data integration synced to your
  entire software stack?
• You can also choose a mixture of two or more data
  integration software types based on your
  requirements. The right integration software
  will provide you with consistent data management
  for the data flow. It will also sync data
  throughout your software stack and restrict
  unwarranted access to confidential data.
• The Endless Nature of Data Integration
• Data is an integral part of the efficient
  functioning of any business. Your Sales team
  will depend on consumer data to analyze consumer
  behavior and prepare sales strategies
  accordingly. For data- dependent companies like
  yours, the data integration process can never
  cease. As technology advances, so too will the
  data integration

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process. The advanced data integration process
will bring about new challenges. To mitigate
these challenges, companies will require robust
and flexible data integration software with the
potential to scale. About Ciente ? With
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