Analyzing all information sources can provide valuable insights, identify areas for improvement, and optimize resources for better business decisions and profitability.
The data economy has taken the world by storm and, in the last decade, it’s become a commodity for most organizations. In fact, data overtook oil as being the world’s most valuable resource in 2017, according to The Economist.
Businesses across industries market themselves as ‘data-driven’ enterprises, yet their collected data is widely underexplored, and even in some cases cast aside or forgotten. There are many reasons corporate data is discarded but, in this current AI/ML and Cloud data management world, harnessing this data unlocks opportunities for growth, diversification and efficiency. Companies simply need to uncover it, understand it and put it to work.
What’s holding companies back?
For organizations, their resistance comes from a combination of reasons. First, it’s the leap of faith. Businesses often don’t know what the insights are going to be when they start their data projects.
Another challenge is that mining data isn’t cheap. Data can be messy, and unless you know what you’re looking for, metric analysis can yield inconsistent results, making it difficult to derive accurate insights. Incomplete or inaccurate data can lead to poor decision-making, which can negatively impact a company’s bottom line.
Data processing also requires specific skills, such as analysis, visualization, and programming. Companies often face a shortage of skilled personnel who can manage and analyze their data in the Cloud and derive meaningful insights.
Making the most of all data
Operating in the Cloud can remove some barriers by enabling businesses to store more data at a much lower cost. As a result, businesses can keep data previously classified as less important or low-level that would have previously been dismissed. In data analysis, low level doesn’t necessarily mean low value. Companies in all industries are realising how valuable this data can be when used correctly.
For a pharmaceutical company, for instance, not only can its SAP data help it meet track-and-trace requirements, but it also allows them visibility into every movement of their product through the supply chain. They can even visualize these insights over time and produce animations of the flow of the product through the chain. Now, with additional metrics and analysis capability, they’ve optimized their supply chain and have a much greater understanding of what’s happening in real-time across worldwide operations.
While often labelled as “boring data” because it lacks detail, when integrated with external insights, some SAP data suddenly becomes highly valuable.
An example of this is a global beverage company that looked specifically at production waste data. Historically, this level of data was rarely integrated into regional operations. However, this firm combined production waste data alongside current and historic weather pattern data to identify potential improvements to their production. Based on this analysis, the firm created models showing that if it rained too heavily, production efforts would be wasted as rainfall affected the color of the beverage, making it unusable. What was once discarded became highly valuable information that could save them eight figures in lost revenue in wasted product.
Getting started
Upfront investment costs deter some organizations from making the leap and investing in data analytics. Fortunately, the hyperscalers can offer a great deal of support in this area. They have experienced teams that are skilled in data capture, analytics and reporting – across all geographies and industries. They can provide Proof-of-Concept to show you what you might be able to get out of your data, which can make the initial investment less intimidating.
Once you make the decision to take the next step, remember these tips to ensure you derive the most value from your data project:
- Focus on collecting low-level data in addition to what is deemed to be ‘priority data’ and then structure this intelligence in a readable way. There may be important untold insights to be gained from processing what was previously written off as useless or discarded due to data storage costs.
- Close scrutiny and oversight across your operations in combination with the latest Cloud technologies and tools can provide you with new ways to visualize your data and identify potential efficiencies and improvement.
- Data quality and consistency is critical for deriving accurate insights. Data cleansing and normalization processes, as well as data relevancy and governance policies, are needed to ensure data consistency. It’s equally important to invest in skilled personnel like data scientists and analysts.
- Use machine learning to run models on your data to identify insights and maximize its value. Integration of that data with other data sources can take it that one step further.
The return on your investment will come in the form of optimized operations, more informed decision-making, fast and accurate forecasting and much more. The Cloud migration services market is set to grow to over $20 billion by 2028. And by then, who knows what new innovations we’ll see uncovered from reviewing what we’ve been discarding for so long.
