The modern business landscape is in a constant state of flux, making it crucial for businesses to adapt and evolve. One way businesses can stay on top of these changes is by utilizing external data. This data, gathered from sources outside the company, can provide invaluable insights and help businesses make informed decisions.
However, leveraging external data is not without challenges. This article will explore the importance of external data, the challenges it presents, and how businesses can overcome these challenges to reap the benefits.
The Rising Importance of External Data
In today’s digital age, data is often referred to as the new oil. It’s a valuable resource that, when used properly, can fuel a company’s growth and success. External data, in particular, is becoming increasingly important. A survey from the BARC Advanced & Predictive Analytics User Study in 2017 revealed that two-thirds of companies were already utilizing external data sources for analytics, with around 30% even purchasing external data [1].
Several types of external data are especially relevant, including weather data, spatial data, social media data, weblog data, and demographic data. These data types have a wide range of sources, varied use cases, and numerous technical solutions to facilitate their use.
The rising importance of external data is also evident in the increasing number of dedicated data marketplaces. These platforms offer access to a wealth of external data, helping businesses to gain a competitive edge. Companies like Datarade and Quandl are prime examples of such marketplaces [2]. Another clear indicator is the incorporation of data marketplaces by big cloud platforms such as AWS, Google and MS Azure. Database vendors Databricks and Snowflake provide their own data marketplaces. And even SAP, Bloomberg and Nokia are among the big names in the data marketplace game, alongside a multitude of specialized marketplaces catering to specific industries.
The Challenges of Using External Data
If an appropriate use case and corresponding data types have been found, it is essential to discover how this data is to be procured, processed, analyzed and integrated into the company’s data household. So, the most important challenges are the identification of relevant sources, the technical integration, storage, integration and analysis.
Relevant data sources are, e.g., open data. Open data are data that are available free of charge and their use is not subject to restrictions. These include above all demographic and spatial data provided by local governments, countries and universities. Another data source is data provided by companies, which although proprietary, are released under certain conditions for free for use by third parties. Here, it must be considered that the free use of the data is subject to usage restrictions. Data markets offer a non-gratuitous use of external data. Examples are Quandl or Matchbook Services.
Once the necessary external data have been identified, they must be accessed and integrated technically. Access to certain public data sources is integrated into some software packages. Data integration tools, such as those by Talend or Informatica offer connectors to social-media sources, the advanced analytics platform RapidMiner contains a connector to a linked open data project and Microsoft Azure ML is highly integrated with its own marketplace. Oracle also provides a multitude of data via its Oracle data cloud.
The storage of external data can be made more difficult by the fact that data is polystructured, reaches a large volume or is subject to short update cycles. What complicates things is that for analyses not only the current (data) state is relevant but rather a history of this data is often required. Most data providers have recognized this challenge and also offer companies data storage services.
To be able to analyze data from various sources, a homogeneous data stream must be created from heterogeneous data sources. On the one hand this means that different, heterogeneous external data sources must be integrated and on the other that internal data must be enriched and matched with external data. Several companies offer support here as well, by having completed this integration already. Depending on the company and application, the focus is on different data types.
To draw findings from external data for analysis purposes these data must be analyzed in conjunction with internal data. Alongside visual analyses, data mining methods are used to identify customer clusters, influence variables and purchase probabilities or to predict volumes. For this, the functions offered by an advanced analytics platform can be used. Beyond that there are a variety of software solutions that offer ready-made analyses for specific use cases.
Overcoming the Challenges: Strategies and Solutions
Despite these challenges, there are strategies and solutions businesses can employ to leverage external data effectively.
One approach is to leverage open data. Open data refers to data that is freely available and not subject to usage restrictions. This data, often provided by local governments, countries, and universities, can be a rich source of external data [2].
Businesses can also turn to data integration tools. These tools, like those offered by Talend or Informatica, offer connectors to various data sources, making it easier to access and integrate external data. Advanced analytics platforms, such as Microsoft Azure ML and Oracle Data Cloud, also provide a multitude of data, easing the process of integrating external data [1].
The Role of Data Marketplaces
Data marketplaces serve as a crucial gateway to the world of data. They are platforms that enable the exchange of data between data producers and consumers. By structuring the data supply and increasing transparency, data marketplaces simplify data access and promote data collaboration [2].
Data marketplaces are particularly beneficial for obtaining external data. They provide an organized and user-friendly platform for data consumers to access a wide range of data products. Moreover, data marketplaces streamline the process of data shopping and shipping, making it easier for businesses to acquire and use the data they need [2].
Conclusion
In the fast-paced, data-driven world of business, external data is a game-changer. It offers businesses access to a wealth of information that can drive growth and success. However, leveraging this data presents several challenges. Businesses must navigate issues related to data identification, integration, storage, and usage restrictions.
Fortunately, strategies and solutions like open data, data integration tools, and data marketplaces can help businesses overcome these challenges. These simplify the process of finding, acquiring, and integrating external data.
In the end, the key to leveraging external data lies in embracing these solutions and strategies. By doing so, businesses can transform the challenges of external data into opportunities for growth and success.
[1]: BARC Advanced & Predictive Analytics User Study, 2017
[2]: BARC Whitepaper Data Marketplaces, 2022
