- ctepl
The wide availability of on-demand cloud computing and cost-effective storage has transformed the way businesses operate. In such a continually evolving environment, every company needs a reliable, real-time, data-driven strategy to remain competitive and stay ahead of the curve.
Sadly, legacy enterprise systems do not support this kind of scale or agility. To capitalize on your customer base and extract maximum ROI, you need to manage, analyze, and monetize enormous amounts of multi-modal data from various sources in different formats. For this, you need a combination of Artificial Intelligence, and Machine Learning (AI/ML) enabled predictive and prescriptive analytics.
But research shows companies are struggling with this.
Data warehouses are expensive to maintain though they are the backbone of any business application or operation. Realtime access to business-critical information makes data warehouses unique and costly. The higher performance of data warehouses demands higher costs. Even the price of having cloud warehouses can spike up based on the kind of workload that is run. The more demanding the analytics and models, higher is the cost. This also makes cost quite unpredictable. It can be estimated that around 70-80% savings can be made just by optimizing the period of access to data warehouse services.
Gartner¹ reveals that more than 80% of chief audit executives (CAEs) believe that their company will lose competitive advantage in 2020 if they don’t utilize their data properly. Also, due to the massive reduction of physical interactions due to COVID-19, unlocking value from digital data is even more critical.
But companies that still use traditional enterprise data solutions find themselves lagging. In a conventional DWH (data warehouse), schema and pre-defined structures are applied to the data beforehand for easy and fast SQL queries. This is excellent as long as you already know the data sources as well as how you plan to use it. But what if business data includes both structured and unstructured datasets in large volumes and velocity including social media platforms ? You will need to rethink your data strategy.
Imagine a financial services company. If they only take data from direct usage, they will only know what customers have spent money on using their app, services or product. It will not tell them the customer’s location, behavioral patterns, etc.
Meanwhile, a competitor who has access to structured data like CRM and transaction data can couple it with external, unstructured data like users’ internet activity, shopping trends, mobile usage, and perhaps even insurance and investment data to target their ad campaigns much more effectively, gain the customers’ trust quicker, enable faster conversion and overall maximize their ROI in a way that would have been impossible with traditional methods.
Similarly, a utility company that only relies on historical data deploys its resources, budgets, and equipment reactively – after the fact. However, using AI/ML and combining data from various sources, including weather and geospatial data, they can predict outages or equipment failure and therefore deploy resources before the fact, resulting in massive savings from unnecessary replacements, etc.
If you want to do all this, you need a completely different way of storing, researching, and analyzing your data. That’s where Cambridge Technology’s expertise with Data Lakes comes in.
A Data Lake is a type of enterprise data warehouse (EDW). It’s the next generation of the traditional DWH, an agile management solution in which vast volumes of structured, semi-structured and unstructured data are stored in their native formats. This enables you to use this data for all kinds of real-time analytics and on-demand solutions. Further, the cost is much lesser than for a traditional DWH because pre-processing isn’t required. However, there are issues here as well, because many companies simply dump a lot of data into Hadoop. But they never think about findability, organization or governance.
This creates a data swamp, not a data lake. But with Cambridge Technology’s expert solutions, you will have a customized data lake with adequate governance, proper classification using metadata, and adequate contextualization.
Now we come to the next stage of the problem. Many companies simply cannot get vast stores of data about customers and their behaviors just from direct interactions alone. That’s why you need access to third-party data from a trusted source to enhance and improve the information you already have – this is called data enrichment.
Companies in the US spent $11.9 billion in 2019 alone on third-party data, says an IAB and Winterberry Group report² This is what they spent it on:
So where can you get your hands on all these different kinds of data, that too from trusted sources that are systematically verified and regularly updated? Through our trusted partners, AWS Data Exchange.
AWS Data Exchange is a vast repository of third-party data across healthcare, media, entertainment, financial services, location/geospatial data, and more. The data is collated by some of the most reputed names in each industry, including Reuters (media), Dun & Bradstreet (business transactions), Change Healthcare (healthcare), and Foursquare (location).
When you subscribe to any of AWS’s data products, the API allows you to load that data directly into Amazon S3. Further, since this data is on the cloud, it gets integrated with your data lakes and analytical platforms.
As one of the leading providers of AI and machine-learning-based predictive data analytics, Cambridge Technology has helped numerous companies in the US and beyond to completely transform the way they interact with customers and enhance the value they derive from their business.
Watch this webinar to learn how you can build Enterprise Data Warehouse and Data Lakes on AWS.
Contact us today to learn more about how we can help you monetize your data better by building a data lake.
