Integration

Decoding Data Integration Challenges- What Are We Really Afraid of?

Integrating data is one of the thorniest challenges in business intelligence and analytics”. Analysing challenges discussed 20 years ago, versus those recouped now, it is easy to understand the apprehension that lies within the consumer to integrate data analytics into their company protocols. The issues? Extremely basic. Read more to understand the root of why companies, even those deep instilled in tech, are finding it difficult to adopt data integration as a business tool.

Businessintelligence #dataintegration #dataanalytics

Slithering right out of the pandemic, most companies take pride in having acclimatized to the trend of making numbers their biggest assets. Almost a decade ago, tech-giants predicted that data integration would come with challenges, obvious to a field that was expanding to encapsulate and be able to predict the entire spectrum of human behaviour.

Compared alongside the issues of 2022 and the challenges predicted almost a decade ago, there are two noticeable basal issues that are the root cause of this static.

Dependency: It always feels expensive

The increased size and complexity of corporate IT landscapes essentialized big data processing. 77 percent (respondents to a Corinium survey) said that processing high volumes of data is at least ‘quite challenging,’ and 73 percent indicated that their teams find it at least “quite challenging” to deal with multiple data sources and complex data formats. The Corinium report also states that some data teams spend as much as 80% of their time in cleaning, integrating, and preparing data for use in analytics.

Staffing, Literacy, and Software systems all took a fortune to employ and internalize. Fortunately, with the last decade rather automating innovation, thousands of SaaS, DaaS and PaaS companies have adjusted their propositions to facilitate change from the ground level up, with 0 dependency and commitment to their service. Dependency is a huge factor that spikes up costs in the mind.

The initial cost for internalizing a process is always high, but overall, even the internet was an evil thing before it essentialized in the corporate, and personal world.

https://venturebeat.com/2022/02/07/report-88-of-it-execs-say-integration-challenges-are-slowing-digital-transformation/

Re-Education: Facilitating a Cultural Change (every single time)

The human mind evolves, but will the system? Future-proofing operations essentially hits back on the nerve that worries about cost (rightfully so).

Humans tend to trust their instincts and their ‘gut feelings’ about what is right, and what is wrong, as opposed to a central truth, which data integration aims to provide.

“22 percent say staff generally don’t trust data-driven insights, and 44 percent report that staff won’t trust insights from data that don’t confirm their ‘gut feels’”. (Corinium)

This widespread lack of trust in data-driven decision-making is a huge concern across organizations, where almost every industry will continue to become more reliant on data.

Indians especially are famous for their preference of double checking and manually managing data.

The solution to this, is effective governance, standardization, and operational literacy.

  • Real Time Governance through teams specially dedicated to quality checks and facilitation of unbiased, clean, data.
  • Valid(ate) data ingestion through a centralized, standardized, and assisted data entry system. This ensures that there is thorough protocol, leaving no team left behind when indulging in BI.
  • Disparaged data pools is a thing of the past; data tools are much more precise and learned. Consistency, transparency, smarter software’s remove dependency on contractors. Although AI’s data capabilities are still emerging, companies can leverage today the ability to discover connected data, classify and categorise sensitive data, identify duplicates and orchestrate silos.
  • Tools like Zapier, Tray.io, and Automate.io, specialize in automating workflows and one-way data pushes. These will allow you to create trigger-action workflows.
  • Deploying modular architecture helps you implement functionality faster than a typical legacy software model. Tools such as data virtualisation, data mesh, artificial intelligence-enabled data integration, and data fabric –make data and analytics even more effective, and interactive.

Of course, modern integrative tools always seem to sort the issues temporarily, but companies need to enable corporates (clients) to be able to facilitate data integration without having to worry about these two factors every time a new trend pops up.

Can you think of a third factor that causes apprehensions in corporates when adopting data analytics?

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