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How to handle Complex Technology and Systems

There's no point denying it: information technology systems in modern organisations are complex, overwhelming, and challenging to manage. Are you unclear on how to handle complex technology and systems in your workplace? If so, you're not alone.


This article outlines not only how to handle big data but also explains how you can use data to innovate and gain a competitive advantage.



Creation and use of complex data

The world's data continues to grow in size, speed, and complexity. By 2025, the collective sum of the world's data will be 175 Zettabytes. According to IDC, just under half (49%) of data will be stored in public cloud environments by 2025, and nearly 30% of it will be consumed in real-time. Data complexity is growing exponentially.


Customer data is created across a variety of files, contacts, and records. Readily accessible tools provide businesses with a vast amount of information about where and how their customers are interacting and, more importantly, what and when they are buying. The additional information that is now collated by websites and other software solutions gathers the data of our population, who spend most of their time online.


Enterprise businesses must have at least some understanding of the data they are capturing and collating and their responsibilities in handling this information safely and securely. Furthermore, data needs to be able to travel both ways. Tech-savvy customers demand access to their records and have high expectations regarding how their information is handled.


Defining complex data

Complex data refers to sets of records and files that contain many different variables, structures, or relationships. These data sets are likely to include records of a variety of file types, including text (character strings), spreadsheets and tables, and image, audio and video files. Complex or messy data often resides in many different sources.


Complex data is often unstructured, meaning it needs to have formatting and comply with organisational schema or requirements.


Analysing and assessing complex data requires the use of one of a range of special software tools. Integration tools enable data from multiple sources to be able to be compared and contrasted. Data management tools include Enterprise Resource Planning (ERP) and Master Data Management (MDM) tools.


ERP systems harness records and consolidate them into a more consistent and helpful format. MDM systems assess complex data sets and create a master record to reduce the risk of errors or problems caused by duplicate, inconsistent or versioned records.


Complex data vs big data

Complex data and big data are similar concepts but have slightly different definitions. As outlined, complex data contains a variety of file types, usually in mixed formats and from multiple sources. Big data is any set of records or files that are too large to be handled simply through traditional data processing methods. Big data is recognised by the scale of the 5 Vs—velocity, volume, value, variety, and veracity.


While big data may be complex, not all complex data is necessarily defined as big data. Big data often becomes problematic for organisations because of the advanced tools and techniques needed to process and analyse it.


Business challenges in complex data

Unfortunately, most organisations operate without clearly defined data use protocols and pathways. They are creating a massive amount of data that users need help to effectively access or use in a meaningful way. The need for better handling of complex data is driven by both reactive and proactive forces.

Organisations must improve data handling to be responsive to changing compliance requirements, regulations, and guidelines. System security is often a reactive driver of change; while taking a proactive approach to complex data can help you advance your market position in a competitive environment.


What is data engineering?

Data engineering is a process that begins with raw data files and transforms them into consistent data sets and records that other applications and processes can more easily use. It creates data that can be used to support machine learning and complex analysis. Software engineers carry out data engineering, extracting data from any number of source systems and managing its transformation into what is called serving data.


Data engineering enables other data users from around the organisation to access and analyse data that is too difficult for them to generate or access without engineering support. The engineered data is made accessible in core data sets, which meet user requirements. A data engineer can customise a core data set to suit the needs of an individual or team. The core data sets are accessible for the average employee to work with - and are prepared and presented in a way that can be easily understood.


Core data sets can also provide a historical perspective over a period of time, making it easier for users to track change or growth. Data engineering is a form of data preparation.


What is data preparation software?

Data engineers may use data preparation software to carry out data tasks such as:

  • Data discovery
  • Data blending
  • Data collating
  • Data cleansing
  • Data transformation

Data preparation involves preparing mixed data for use in business intelligence planning. To qualify for inclusion in the data preparation category, a product must:

  • Allow blending, combining, and transforming datasets for simple integration and analysis
  • Improve data quality with cleansing and enrichment capabilities
  • Integrate with analytics and data integration solutions
  • Enhance data preparation capabilities as a standalone software or when integrated with an analytics platform

Examples of data preparation software include:

  • TIMi Suite
  • Altair Monarch
  • Trifacta
  • Alteryx

What systems and software help data innovation?

There is a clear correlation between complex data and a business's ability to innovate. If you have big data being generated and stored, you have a responsibility to do what you can to use that information. Why just deal with complex data when you can use it to innovate and scale?


Creating the right stack of solutions and tools can help you accelerate innovation by processing, refining and presenting data that can help you gain insights and spot opportunities as they arise. By giving your staff the right tools, they are empowered to grow and improve.


Various software tools have been designed to help data be used for innovation. Data innovation can occur across several categories of work and through different types of data software. These categories often overlap with other solutions in a stack, such as a CRM, DXP or CMS. The following examples are unique tools that can be integrated with existing solutions.


Collaborative and creative workspaces - Creating shared spaces for employees to communicate, share ideas and collaborate on creative opportunities is a great way to enable innovation. Collaborative workspaces have been proven to help employees think outside the box, unlock and exchange collective knowledge and boost productivity. Collaboration tools are helpful for teams with remote workers or staff located in different parts of the state or country.



Collaboration tools often integrate with other projects or productivity software. Team collaboration software can include screen-sharing solutions, intranet software, and virtual workspaces.


Popular examples in these categories include ClickUp, Miro, Zoom, FigJam, Mural, Yammer, SharePoint, Nooks and Gather.


Planning and project management tools - Often handled by project officers and managers, project collaboration software supports project planning, coordination, and management. It allows users to share documents and manage version control. Access can be universal, or teams and workspaces can be set up.



Task management features enable users to create tasks and organise activity by status, priority or section. This can include Kanban boards and Gantt charts. Shared calendar and timeline tools also make project collaboration easier to keep on time and in line with the critical path.


Popular examples in this category include Smartsheet,, Asana, ClickUp, GanttPRO and CMap.


Feedback and engagement tools - Feedback analytics software and community engagement tools consolidate feedback from customers and staff. Feedback and suggestions are often the originators of innovative ideas and solutions. Survey software facilitates the collection and analysis of data through the creation of surveys, quizzes, and polls.



Popular examples in these categories include SurveyMonkey, Qualtrix, Microsoft Forms and Typeform.


Enterprise feedback management software - These tools collects customer comments and contributions and transform them into actionable reporting and data sets. These tools are often integrated with a CRM and support market research from within a website.



Popular examples in this category include HubSpot Service Hub, Glassbox, Salesforce Service Cloud, Verint Voice of the Customer, Aha! and Dynata.


Idea management tools - Ideas management tools provide your organisation with a dedicated space to detail and consider new ideas. These ideas relate to product development or prototypes, marketing opportunities, document proofs, people and culture opportunities or any other new or novel way of carrying out your work. Idea management software can be integrated with other enterprise software, and some solutions even enable external or customer contributions.



The most popular idea software tools as ranked on G2 are:

  • Ideanote (4.7-star rating with 267 reviews)
  • Brightidea (4.3-star rating with 203 reviews)
  • UserVoice (4.5-star rating with 164 reviews)
  • Aha! (4.3-star rating with 73 reviews)
  • Wazoku (4.4-star rating with 71 reviews)

Machine learning in complex data

Machine learning is a process that uses Artificial Intelligence (AI) to process and transform data. It can be used to improve data handling processes through algorithms. Machine learning technologies can handle very large volumes of data. In fact, the more data these tools can analyse, the more efficient and effective their processing becomes. As cloud computing increases, more organisations now have access to machine learning capabilities.


Migrating complex, unstructured data into the cloud or a hybrid cloud location via machine learning can help your business. Many organisations need access to adequate data-handling infrastructure on their premises. Rapid data growth, budget limitations, and a lack of in-house expertise in handling complex data mean that many organisations need to look for external and cloud-based storage services.


Machine learning can transform complex data and relocate it to the cloud. Cloud-based complex data storage can:

  • Enhance overall data compliance and security
  • Meet data compliance standards
  • Improve customer experience
  • Provide access to better quality and recent data


Pimcore and complex data

Pimcore is a Product Information Management (PIM) and Master Data Management (MDM) tool optimised for handling large product datasets.


Master Data Management tools like Pimcore can help an organisation to address the challenges presented by complex or unstructured data. An MDM tool can help your employees access the information they need when needed, which creates opportunities for innovation and improvements. An MDM tool offers:

  • Better data quality - through the production of a reliable data set that tidies up, consolidates and organises unstructured complex data
  • Integration with Enterprise Data - an MDM solution can be used to parse and link data that is accessed by API, which calls upon and display records from other platforms
  • Data accessibility - Running a search in Pimcore delivers accurate and quick results- no matter your collection size
  • Enhanced data security - MDM solutions can identify and handle duplicate or contradictory records, giving you the confidence that your records are up to date


Pimcore can manage multiple and separate record versions, enhancing the accuracy and control level of your files. These practices enable you to deliver quality data governance and stewardship. Pimcore can also support other security processes including user authentication, file encryption, access logs and reports on identified deviations and breaches.


In the current economic environment and in the future, companies will have to rely on plenty of accurate data and information in moving forward for them to make good management, marketing, administration decisions for their organisations. Let the team at Stimulus show you the way and help you improve your handling of complex data and files.



Related Questions

What are the best tools for storing complex data?

An effective data storage solution and process can help you manage complex data effectively. If you have an effective data storage solution in place, you are less likely to experience significant problems in the event of a data hack or system failure.


According to this report by TechRepublic, some of the best enterprise data storage solutions are:

  • Best for flexibility: Amazon Simple Storage Solution
  • Best for on-site data backups: IDrive BMR Pro
  • Best for SaaS data storage: IBM Storage Protect for Cloud
  • Best for Google Cloud users: Google Cloud Storage
  • Best for hybrid data storage: NetApp StorageGRID
  • Best low-cost storage option: Wasabi Hot Cloud Storage

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