With all the hype around big data, it sometime seems that everyone is doing it. But the truth is that implementing big data analytics can be almost as difficult as it is potentially profitable.
The secret about big data is out, and the race to become a data-driven company is on. Management consultant companies are in overdrive to get their clients working on data analytic projects to improve margins, boost revenue, cut costs, and make customers happy. Just as the introduction of the database in the 70s helped to revolutionize back-office processes, the rise of big data technology is remaking how companies across all industries interact with customers and each other.
Top 5 Big Data Myths
Theres no doubt that big data is a big deal, but there are some hitches that threatens to derail the big data wagon before it really gets going. Here are the top 5 myths about Big Data:
1. Everyone is Ahead of us in Adopting Big Data
This isn’t true, while there are many companies talking about Big Data, Gartner found that only 13% of companies surveyed had actually deployed any Big Data solutions. This is compared to 73% declaring investment, or plans to invest in some form of Big Data strategy. So don’t worry, you aren’t lagging behind everyone, but you might want to start acting rather than just talking about it.
Maybe start small, roll out a proof of concept and see how it can benefit your business to make sure you aren’t left behind by your competitors.
2. Lots of Data Means Good Data
No it doesn’t. Your data could be absolute rubbish that may negatively impact your business if you could get it to tell you anything. Poor quality data could be riddled with errors and missing data, which could be misleading.
Don’t make the assumption that just because you have a lot of data, it’s going to be automatically great. You need to have an intelligent model that can sift through the data to make sense of it and advise what to keep and what to chuck.
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3. Big Data Can Tell You the Future
Big Data can guide you to a more accurate prediction of what the future might be, but it’s not going to be 100% accurate.
The best solutions will be able to tell you the probability of a number of events based upon the historical data that you have fed to your solution. Not only is this dependent on your data, but it is also dependent upon the questions you ask. If you apply analytics with a lack of precision or detailed hypothesis then you could be lead astray.
4. You Need a Data Scientist
Yes and no. In an ideal world every company could have a Data Scientist to guide their hand with decision making, but this isn’t the case. Many tools have been created to make your SAP, Salesforce or Tableau platform capable of making intelligent and informed decisions that can guide you without needing a data scientist.
One way to get around not having a Data Scientist is to outsource your analytics to someone that can do the work for you, say like pure play analytics firms.
5. Big Data Means Big Costs
Not really. Of course this is relative and depends upon what kind of solution you go for but in most cases it isn’t expensive. Technologies like Hadoop and Spark can be very affordable, while outsourcing your analytics needs is one way to avoid costs.
Virtual Hadoop platforms like Qubole in the cloud can eliminate the expense of physical servers and warehouse space. Also the pay as you use model is one that is becoming more common with Big Data platforms, so you only need to pay for what you use, when you use it.
Warming up to Big Data
The big data movement is still in its infancy, while Hadoop heavyweights like Cloudera, Hortonworks, and MapR Technologies are giving us plenty of sandboxes to get started with analytics and even lightweight application frameworks to jumpstart specific use cases like fraud detection and making personalized recommendations, getting a big data analytic application built and deployed requires a lot of handholding today.
Change Management
Change management is another big headache. How do you get people to utilize a different model of execution? Theyre all used to doing things in a certain way and getting them to do it a different way requires effort. You put in these solutions, the question is, will they use it? The real problem is adoption.
This dynamic will change as big data analytics becomes more commonplace and people who are familiar with the technology move into leadership roles. And as the big data software gets better and easier to use, people will naturally warm to it and become more accepting of the big-data way. (Resistance, as the Borg said, is futile)
The 80/20 rule of Analytics
The 80/20 rule applies to the analytics solutions, i.e. 80% of the time goes into prepping the data and 20% of the time goes into actually using it for any sort of analytics.
No doubt that creating value from big data is a stumbling block with no easy answers but the winners in todays conversation economy are the ones who understand the way in which value is created after all its not about operationalization of information rather its about operationalization of intelligence. What form does the data take when it transforms from information to intelligence and how do you operationalize intelligence. Thats the real issue for organizations and business users.
The need of the hour is to reverse the 80/20 equation and let customers spend much more time doing actual analytics instead of wrangling and cleansing the data, Big Data and Machine Learning is a step towards this.
Whats your take? Chime in with your thoughts below.
