1. Big Data: As good as you want it to be
The value of big data lies in its use and reuse. Often, the value is unleashed when very different datasets are combined to collectively answer big questions. With big data, the answer is more valuable than the sum of the data.
2. Not grasping data complexity
Big data is complex. It might seem simple while inspecting the data from an end user perspective, but that simplicity hides the multiple complex layers of relationships and data sets. The complexities are present in the data itself because of its structure and formats, content, and metadata. Without understanding the complexity, modeling a solution for the data set—whether statistical, mathematical, or text mining—can create erroneous results.
It becomes excessively important to keep all such considerations in mind when developing and implementing big data solutions. Without this in mind, your organization could be missing out on a lot of trends and projections otherwise hidden in the data.
This gives us a very important learning – while data might not seem important in the beginning, it doesn’t mean its a waste. Down the line, such data can unfold in ways never imagined, and lead to great results.
3. Treat it like an asset
Businesses drive on data. It is one of the most important resources for an organization, probably second only to the people working there. There are ample examples of companies making big decisions based on big data analytics, which for the most part, drives the businesses to greater heights. Such decisions depend upon precise and timely data, leading to correct or beneficial decisions. Organizations need to manage and process data so that it is available to them at the right moment in the right form.
4. Quality of Data
Poor data quality can ruin analytics. For big data, overall data quality can degrade as unstructured and semistructured data are integrated into data sets. Improving data quality is an important consideration for processing big data. Without taking this step, the output often results in skewed results and can negatively impact the analytical systems in the enterprise.
5. Big data is risky
There are risks associated with big data which cannot and should not be ignored. If information is power, then insights based on data from everywhere that predicts future behavior is the type of power that can corrupt most. Predictive analytics is based on numbers and how we interpret them. Results can be biased, numbers misleading and algorithms misanalyzed. Understanding this and the inherent limitations of big data are crucial to mitigating risks.
6. More is not always better!
Is more always better? Talking about data, generally, it holds true. Not necessarily always! More data means a greater amount of irrelevant data, more mess, and higher resources needed to make sense out of it. Looking at it from the other sire, it also means better projections, more information and lower error rate. It is a delicate balance to be maintained while dealing with big data analytics.
Why not! Data is yours to keep, it’s a raw ingredient that can be used multiple times, as long as you desire. Similar or even same data can be utilized to solve multiple problems by approaching it from a different angle. Even historic data is a very apt example of how it can be reused in ways unimaginable at the time of data collection.