Does your organization want to lower data storage cost while improving analytics output?
Data lakes can reduce long-term storage costs while improving what data is analyzed and communicated.
Even if you do not require big data storage capacity, a data lake is a valuable addition to your IT infrastructure when you already have an Enterprise Data Warehouse. In the following post, we will outline the substantial benefits data lakes can bring to your IT infrastructure.
Increased Storage Capacity
Data lake architecture enables enterprises to keep all of their data in any format desired. In the past, keeping all of your data in one central repository was not possible or financially feasible; therefore, the data was aggregated or rolled-up to summary information such as transaction counts in quarters or years. The fine-grained detail was deleted after aggregation to make room in the staging area to roll-up new information. By storing all the details in the lake, they can be analyzed and explored as often as desired without any loss of fidelity in the future. Data lakes encourage retention of all data because they have the capacity and it is cost effective.
Lower Costs
When compared to storage and analytics solutions before the rise of big data, the total cost of ownership per byte of data was higher, and the technical sophistication was lower in the past. Today storage and processing costs are low due to many factors such as competitive cloud pricing, compute and storage elasticity, IT automation, and smaller resource requirements for each node running big data technology. As previously mentioned in our post comparing at data lake and data warehouse, the initial investment to create a team to manage and use a big data infrastructure is higher than EDW counterparts. But cost savings (per byte of data) will be higher at scale since a smaller team can manage the data as it grows. Therefore, data lake technologies can scale instead of your team.
Integrated Value Chain and Omni-Channel Experience
Unlike a warehouse, a data lake allows you to store all kinds of data. The majority of data today is unstructured as opposed to structured. These formats of data have been stored for decades, but not together and not with industry standard methods and technologies to analyze them as one cohesive set of data. Data lakes allow organizations to centrally collect, process, and analyze all formats of data quickly and cheaply. Data lakes fuse data that includes internal, external, client, competitor, social, and business process information. They allow data engineers to mine essential and advanced metrics. Organizations can have a better understanding of the effects of their decisions on customers, suppliers, competitors, partners and any player in an integrated value chain.
Your organizations (or clients) are competing for the attention and dollars of customers. Your likely using multiple channels like SEM, email, website, blog, Facebook, Twitter and other social media. But are you providing an omni-channel experience for your customers?
Omni-channel accounts for each platform, the point of interaction, and device a customer will use to interact with the company. These channels should be integrated to track the customer from the first touch to purchase. The customer may be shopping in a brick and mortar store, from an online desktop or mobile device, or by telephone and the experience should be seamless. This omni-channel experience also allows your organization to provide a more comprehensive view of or to customers — an experience that ties all data sources together from multiple interactions to offer them the best experience.
A data lake is best suited to persist in every detail of the experience, retell this story and provide a technology stack to predict what an individual customer (not a broad demographic) will do next. This level of experience management can be applied to suppliers, partners and other players in your value chain.
As we have outlined, data lakes can provide a significant amount of value to your organization, from costs saving to the customer experience.
In our next post, we will describe how data lakes can be paired with existing data warehouses.
If you missed the first two posts on Data Lakes, here are the links:
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