Exponential data is everywhere. You carry a good chunk on your smart phone wherever you go. It’s at home where your security system records who comes and goes. It’s at the office in the multitude of corporate information systems. And it’s on the shop floor where Industrial Internet of Things (IIoT) technologies have transformed industrial machines into data-generating devices, helping them run more efficiently for longer.
According to IDC’s Worldwide Global DataSphere Forecast 2021–2025, business and consumer data has been growing at a compound annual growth rate (CAGR) of about 23% since last year, with a 28% CAGR attributed to enterprises, and is expected to reach 180 zettabytes by 2025. That’s equal to 23 terabytes of data per person!
Yet, data challenges impede many organizations. A new report from Aberdeen Strategy and Research reveals that businesses are not fully taking advantage of the wealth of available data to generate trusted insight-guiding sound decision-making actions. Among the top obstacles: poor quality data informing decisions, insufficient expertise and lack of cohesive data strategy. In this post, we will examine some of the major challenges companies are facing with the wealth of data they have access to, and how Dassault Systèmes helps them make confident decisions through data-driven insights.
Enterprise Challenges with Data
Challenge 1: Data is scattered across different functions. In organizations, most data is segregated by functional domain. For example, design and engineering data is collected and retained in CAD/CAE systems, manufacturing data is kept in manufacturing execution systems, sales data resides in an enterprise resource planning system, and customer interactions are held in customer relationship management systems. That is not to say data in one system is useless to another – there can be great value when that data is aligned. For example, combining service call data with bill of materials data could uncover quality issues with third-party components and provide business insights when selecting vendors. Siloed data limits organizations’ ability to address and answer broader questions about the business.
Challenge 2: Accessing, aggregating and transforming data. To provide broader business views, companies identify and aggregate data from multiple data sources into a single target source. Aggregating (i.e. joining tables) and enriching data (i.e. concatenation and parsing) are often performed on an ad hoc basis using desktop tools. This may very well broaden the visibility into the business, but it comes with its own difficulties because that data is only a snapshot of the past now stored on a laptop. Is the proper data governance to ensure excellent data quality, data security and data privacy in place for that extract? How are the transformation rules governed? Could rules be used by other groups in the enterprise?
Challenge 3: Information consumption optimized for decision-making. Over the last decade, consumer-grade devices and apps have made data consumption easier and more accessible to everyone. For business users, presenting information has also progressed, going from batch reports to interactive ones, to dashboards and self-service visualizations to predictive analytics. One thing has remained consistent: the purpose of putting together a business data view of your organization is to equip business people with insights to help them make informed decisions. The challenge is to not just present data in a tabulated way and use common visualizations; it’s to create a data-driven experience adapted to decision makers and provide key elements: summary views, filtering capabilities, drill-down to explore detailed information, and more.
Challenge 4: From information to actionable insights. Providing business information in a consumable format is one of the steps facilitating data-driven decisions. But, it is just the starting point of a collaborative process leading to decision-making and actions. The curated information will be shared among team members in order to be explored, reviewed and discussed. Beyond sharing insights, does the analytics solution easily embed the information in communities and processes for further collaboration and discussions? Is the data interoperable with the different applications used by your team? Can you track discussions and actions based on the insight gained? Can you connect the decision, the context as well as associated actions and assignments?
Addressing Data Challenges with the 3DEXPERIENCE Platform
Companies are relying on data science to extract the insights required for steering them on the right path to boost performance across all aspects of their business. As the amount of available data grows, so do the challenges to make better-informed decisions. The 3DEXPERIENCE platform and NETVIBES information intelligence solutions address these challenges by enabling organizations to gather, align and enrich data – whether internal or external, structured or unstructured, simple or complex – using a common, trusted referential to break down silos. Dassault Systèmes solutions allow organizations to build analytics experiences that democratize information consumption across the enterprise. These experiences provide confidence to decision makers because they have complete and accurate information to understand, learn from and capitalize on, leveraging the company’s knowledge and know-how and make informed decisions to drive desired outcomes.
In our next post, we will explore how the 3DEXPERIENCE platform and NETVIBES information intelligence solutions connect the dots between data and the decisions makers, ensuring that all stakeholders – whether designing the next product, optimizing production efficiency or gathering market insight – have all the relevant knowledge available at their fingertips, in the right format, to achieve operational excellence and stay ahead of the competition. Stay tuned!
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