Data management is an integral element of business growth, helping companies make more informed decisions and deepen customer relationships. Unfortunately, however, many organisations struggle to unlock the full potential of their data assets amidst complex business processes, regulatory changes, and governance concerns; without appropriate people tools and strategies in place they may never turn their data assets into tangible benefits.
Businesses aiming to leverage data require a well-thought out vision and strategy that aligns with business goals, customer requirements and technology capabilities. An agile data architecture must also be established so as to support product and service innovation while individuals within an organisation must possess the appropriate skills for extracting and analysing the information efficiently.
Data management has never been more challenging for companies. With the proliferation of internet, social media and mobile devices changing how information is collected, processed, stored and analysed – not to mention artificial intelligence (AI) technologies expanding the amount of available data for analysis – organizations face increased pressure to stay abreast of rapid change by using data as a competitive advantage.
One of the primary challenges associated with IoT devices and sensors is managing and making accessible large volumes of data, known as the “intelligence edge.” This data plays an especially vital role in creating algorithms capable of making predictions and performing tasks otherwise impossible – making an IoT network highly dependent on capturing, processing, storing, managing and making available large amounts of information in order to stay competitive and useful.
IoT devices have created an exponential rise in data, creating significant challenges for IT managers and service providers. These challenges include securely storing and managing this information while offering customers a consistent experience across platforms. Luckily, solutions exist that can help address this challenge.
A quality data management solution should provide users with access to the information they require when and where they need it, including storage of structured, unstructured, streaming and relational data formats in multiple formats (structured, unstructured and relational); supporting various query languages and visualizations; as well as offering an intuitive user interface easily understood by technical and non-technical users alike; meeting regulatory compliance requirements is especially key when managing IoT devices/apps that must adhere to strict data privacy regulations.