Modern companies run on data. We need information in order to make decisions, build new products and better serve our customers. Using data to deliver actionable insights has become a key differentiator for successful leaders. However, the path to accessing usable data is not as simple as it sounds.
Synatic takes a look at data integration trends and how companies are using smart integration technologies to give them a competitive edge.
By 2025, the IDC says worldwide data will grow 61% to reach a staggering 175 zettabytes, and in case you were wondering, a zettabyte is a trillion gigabytes!
Corralling all that information and extracting insights can be tricky but the results are well worth it. In fact, in 2017 the Economist was already describing data as the world’s most valuable resource. For such an important and valuable asset, it is surprising how careless we are with how we store it, keep track of it, and use it.
Data integration enables a unified view of data from numerous sources. It also simplifies the business intelligence (BI) processes of analysis. When companies can easily view and comprehend the available data, they can derive actionable information. What’s more, it allows them the ability to leverage other technologies to enrich those insights. One of these technologies is machine learning.
Growing profitability has become increasingly difficult for companies in a downturn economy. Businesses are no longer simply able to throw extra money at sales and marketing to try to promote growth. In a report looking at how machine learning can assist in growth, KPMG correctly points out that companies need to do more with what they have if they hope to increase customer lifetime value and decrease the cost of customer acquisition. “This can only be achieved by working smarter, making more data-driven decisions, and focusing on the highest opportunity levers and customers,” say the KPMG authors.
Machine learning could hold the key to carving out a competitive advantage. Like its big brother AI, machine learning requires training data. It relies on large volumes of historical information such as sales transactions and other customer interactions. The algorithm can then work independently, and, with constant updating, it can progress to automatically optimizing the algorithms for ever better accuracy. This automation is invaluable, but feeding the data-hungry machine means companies need to access data from every corner of their business. Reliable analytics needs reliable data.
Today, technology leaders are rushing to make use of the power of big data technologies to make sense of complex data sets. However, big data integration can be complex, largely because it often involves huge sets of data that can be structured, unstructured, and semi-structured, all of which need to be accessed from multiple sources.
A good big data strategy needs to consider volume, velocity and variety. For instance, a traditional or batch approach will not cut it when it comes to high volume integration. If you want fresh and up to date insight, you need to invest in real-time integration.
Similarly, the variety of data generated these days needs a data integration solution that can natively handle multiple data formats. Likewise, the speed at which data is processed (velocity) can also pose a challenge when it comes to integration. Working with an integration specialist will de-risk your efforts and ensure your data is fresh and accessible for your data scientists to make the most of.
The world of data analytics changed in March 2020. As we went into lockdown, decades of historical data became meaningless for many organizations. Traditional data models lost much of their meaning and data science (aided by AI) is now turning to small and wide data to help determine business patterns.
As its name suggests, small data is data in a volume and format that makes it accessible, informative and actionable. Wide data, meanwhile, allows the analysis of a variety of data sources. It also handles a wide array of data formats including text, tabular, audio, image, video, and even smell and vibration.
The uses for these types of data are huge and include the complex realms of demand forecasting, CX and hyper-personalization in customer service.
So great is the growing need for this new kind of data, Gartner has predicted that by 2025, 70% of organizations will shift their focus from big to small and wide data.
Whether big, small or wide, the meta theme for IT leaders this year is clearly integration. Their first and most important job is to make data available to analysts and data scientists - and even machines - so they can glean insights that can help drive new innovation and profitability. Fortunately, with a modern integration platform, CIOs can enhance the value and usefulness of their data assets, delivering new products and services that meet their customers’ needs.