Decoding Enterprise Data

  • July 27, 2023

By this point, enterprise leaders are well aware that data is king in the 21st-century economy. But, despite collecting massive amounts of data, most companies struggle to make the leap from data to insight.

As a result, companies are racking up digital transformation costs with little to show for it.

Drowning in data, starving for insights

  1. Data silos limit data’s usefulness

    Over the years, most enterprises have adopted enterprise software systems for various functions. Chances are, your organization has a solution in place for finance, project management, supply chain, customer relationships, and more. Initially, these systems were great. They were designed to solve specific problems and improve process efficiency – which they did.

    But now there’s a new problem: how do you get them all to work together? These systems helped digitalize processes within each department, but how do you move from a handful of point solutions to having a 360-degree view of enterprise data to allow for new, data-driven operating models? The key is breaking down these data silos and integrating all of these disparate systems.

  2. Lack of data literacy among employees

    While many employees have become proficient at using the software tools in their department, analyzing data isn’t native to them. In other words, they lack data literacy.

    According to recent research by Tableau, 70% of employees are expected to heavily use data in their role by 2025, yet only 40% of employees feel they’re provided with the new data skills they’re expected to have.To bridge this gap, companies will need to do two things. First, they’ll need to lower the barrier to entry by adopting software systems that make data analytics as intuitive as possible. This will mean having analytics dashboards geared to what each employee needs so that employees can easily access relevant assets. It will also require easy-to-use interfaces that help you search for the information you need and offer training as part of the professional services package of a system.

    Second, companies will need to invest in upskilling their employees. Even with the most intuitive software, companies will need to invest in upskilling their existing talent with data literacy programs to help employees adapt to a data-driven future of work.

  3. Uncertain ROI for digital transformation

    The writing has been on the wall for a while now that digital transformation will be necessary for companies to compete in the future — and, increasingly, the present. What’s less clear is how to tie digital transformation projects to the bottom line.

    In some ways, this shouldn’t be surprising. The first wave of digitalization with ERPs, CRMs, etc. was relatively narrow in scope. This made ROI easier to measure. For instance, if your marketing team could only send 500 emails a day manually and then started sending 5,000 emails a day with a CRM, you’d simply track the effectiveness of this one variable – increased responses due to more emails being sent with automation – and see if it justified the spend (of course, they likely used more modules, which would help the ROI).

    However, in the movement from siloed systems to enterprise-wide data management and analytics, the ROI can be more difficult to calculate. This is especially true when companies attempt to address multiple problems at once which often involves large upfront expenses and the complexity makes determining cause and effect a challenge.

Making data your secret weapon

As it is, data is more of a headache than a help for most enterprises. At best, they’re retroactively analyzing data and hoping the insights they spent hours finding are still relevant to their current problems. But it doesn’t have to be that way. With the right solution, enterprises can let data drive real-time decisions and use holistic insights to empower continuous improvement across the enterprise.

Enterprises need a way to quickly integrate siloed systems with enterprise data management solutions that are easy to use and offer clear ROI. As we looked around the market, we realized there weren’t many, so we made one ourselves and now offer it in our Data Management & Analytics (DM&A) service.

At the center of our DM&A solution is a data lake architecture that allows for real-time data processing, storage, and analytics. Unlike data warehouses, which typically run reports daily, weekly, monthly, or quarterly, our data lakes allow you to access real-time insights and share this information across your enterprise and with partner organizations.

For example, you can get real-time data on production and share this with customers so they can know exactly when to expect shipments, allowing them to optimize their supply chains.

To maximize the power of these data lakes, you need to connect all your data streams. With other providers, this can be incredibly time-consuming. But not for us. For the past 40 years, Magic Software has led the industry in systems integration. Building off decades of experience and expertise, our solution features dozens of pre-built connectors to all the common enterprise systems. And with an unrivalled implementation team, we can handle custom integrations without a problem. With these integrations finished quickly, we tear down data siloes and focus on the next piece: employee adoption.

We know that any software system is only as useful as it is used, so we’ve put a premium on designing a solution that is intuitive for employees to use. The service comes with several data transformation and contextualization algorithms. With our user-friendly drag-and-drop interface, employees can easily extend/enhance it, even without extensive coding experience. The data is then used by the self-serve analytics of our central workspace to support your decision-making process. This path to increase your team’s data literacy and decrease their reliance on your internal IT team. Additionally, our AI/ML models provide valuable insights, such as anomaly detection and forecasting, further enhancing the platform’s capabilities.

Finally, the industry is filled with multi-year and expensive data analytics and digital transformation projects, which often fails due to uncertain ROI. Instead, we’ve designed a sprint implementation process which focuses on solving one analytics problem at a time in a short 90-day window. This agile approach to implementation decreases the amount of time between concept and rollout, which helps you see ROI as fast as possible. Then, you can use the ROI from one sprint to help fund the next.

Ready to learn more?

If you’re interested in learning more about Magic’s Data Management and Analytics service, contact us today to learn how you can take your next step towards becoming a data-driven enterprise.

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