Unleashing Power of Data

24 | Unleashing Power of Data [ BUILDING MODERN BUSINESSES WITH AN EFFECTIVE DATA STRATEGY ] TOP DATA CHALLENGES AND THEIR SOLUTIONS Data lifecycle starts with data ingestion, which is a complex concept. Organizations receive data from every conceivable resource and in variety of formats. There are instances, when organizations are unaware if they have the right user consent to collect the data, which is a major challenge. Organizations need to think about data velocity, as it depends on how companies monitor the data. Velocity of data requires infrastructure scalability. Companies need to check if they have system in place that can address growing demands. Data quality is the next important aspect. An important thought is that should companies process all the data or skip certain chunks of data and process only meaningful data. The biggest and everlasting challenge when it comes to data is data governance management. Organizations have to keep the data safe, and the challenge arises on managing enormous amount of data. The problem always starts with accessing the data, storing the data and quickly retrieving the data. Small companies do face big data problems. The challenge is how to optimize the data, with the investments we make in technology, as there is competition for capital. EMERGING SERVICE MODELS FOR MID- MARKET BUSINESSES ‘To cloud or not to cloud’ is nomore the debate. Mid-market enterprises are allocating a substantial amount of their budget towards cloud-based applications, which would lead to agility, flexibility, and scalability. From a cloud computing standpoint, function as a service, backend as a service as well as AI as a service is growing in popularity. Startup / SME must consider leveraging technology for their business benefit for faster go-to-market, better personalised experience for customer, differentiating themselves from competitors are the aspects to evolve into modern businesses. If small companies want to capitalize on AI, they can use AI as a service as they cannot build a large data science team. It will require huge investment in terms of resources, infrastructure, etc. Dun & Bradstreet