In part 1 of this series we discussed the confusing landscape of business analytics and the components necessary for a complete solution. In part 2 of the series, we will discuss one aspect of business analytics solutions that is vital for success – the utilization of a data lake. The data lake is a place for all your data sources to enter your governance as raw information and make themselves appropriately available for analysis.
Before we dig into the benefits of utilizing a data lake, let’s take a step back. You might be wondering if you even need a data lake because you have an established database. Though the terms can sometimes be confused for each other, the functions are very different. While a database is used to store structured data from a single source, data lakes can store structured and unstructured data from all sources – say goodbye to data silos! The important distinction of functions is that data lakes enable the ability to derive analytical insights and processes, while a database is used for day-to-day functions that require more buttoned up, structured data. That is why we recommend businesses utilize both. Fortunately, data lakes are designed for flexibility in both scaling and cost.
The key with a data lake is to have flexible tooling for connecting all your data whether it is in the cloud or a local operational database. The goal is to connect to your data, import it and possibly add a tag for its source. Tagging data helps with governance operations downstream when certain data can only be published to limited audiences. For example, you can’t republish some social media data publicly without violating the source platform’s license. For mid-size businesses especially – collaboration, governance, security and enhanced intelligence become important which are all benefits to utilizing a data lake.
The Power of Extracting from Unstructured Data Sources
Another aspect of a data lake is that it enables data from unstructured sources as well. Unstructured data do not have a clearly defined set of rows and fields like a spreadsheet. Examples are text documents, images and video. Being able to extract meaningful data from these sources can provide your solution tremendous power and contribute to answering a surprising number of questions. For example, text can often contain topics which can provide labels for a set of documents or sentiment (positive or negative emotions expressed) that can be quantified as a score. If you are looking at customer reviews, extremely negative or positive sentiment combined with a label indicating a specific product mention can lead to valuable insights. The same could be done with a large body of customer support requests. Images provide a similar value proposition as they often contain text, but also contain objects including important opportunities for labeling like the people represented in the image or even company logos.
Business Analytics, Simplified
The good news is that Blacklight makes this process simple by helping you identify the appropriate solution to meet your businesses needs and ensure you’re meeting your goals without spending more than is necessary. Once we’re aligned with the needs of your business, Blacklight simplifies the process for you even further so that only the details of what you want to do with your data are left to your team. In addition to providing our expertise throughout the solution lifecycle, we also provide provisioned hosted data lakes with a focus on applying analytics to your stored raw data, including unstructured data like text and images. This allows your data to meet its full potential without having to use resources to build and manage a data lake on your own.
Blacklight Solutions offers security, federation of data, and interrogation of multiple data sources. We help our clients unify their business by easily creating a secure data lake that is hosted and scalable to their needs. In part 3 of the Myths of Business Analytics series we will explore the intelligence aspect of analytics.
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