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Last week, we defined the critical differences between “data”, “information” and “insight”, and what it truly means to be data-driven in today’s digitized world. If you missed it, check it out before delving deeper into what today’s post is about: Why so many organizations fail at connecting analytics to action, and how to better equip your team for success when making the ever-critical transition to big data.
By now, the business benefits of big data have been hammered in. Now, it’s time to demystify big data by talking about its challenges and re-examining current frameworks and business processes.
Breaking down the big data problem
So why are 71% of those attempting the analytical leap failing to get real results?
Part of it can be explained by where we are in the timeline of the data evolution: the formative years, Big Data 1.0 . Much like the early days of the Internet, there are growing pains associated with navigating through technologically-uncharted waters. We may understand the transformative potential of big data, but our tools and skill sets pre-date the new analytical approach.
Shortcomings of traditional tools
Too many businesses are hindered by legacy systems, outdated applications, and decades-old storage methods. Attempting to work with big data on a spreadsheet is as inefficient as attempting to fix a car with a single screwdriver.
Today’s outsourced, multi-tiered supply chains require a new system with far-ranging data inputs and the capacity to process it. Excel and ERP systems lack the responsiveness as well as the algorithms to weigh various KPIs across departments, let alone between business partners or the global market.
Fortunately, there now exists a new generation of analytics software that is built entirely around managing complex supply chains through connected data. Many of these solutions are functionally a huge upgrade with the adoption of cloud computing, which makes storage inexpensive and easier to scale.
In the world of big data, there is no place for separate silos of information. Companies aiming to quickly climb through this learning curve should acquire a new tool set that is not only robust enough to handle the volume of data, but flexible enough to enable collaboration of key data sets across and beyond the enterprise.
… But tools aren’t everything
Since tools are intended for use by humans, how they are utilized plays an important role in the outcome of data. A common mistake that stunts an organization’s ability to extract value from their analytics is where the decision maker positions themselves in the process. Often times, they use data to support their own decisions rather than letting it illuminate an unbiased idea, direction, or conclusion. Valuable insight comes from letting the data speak for itself.
Another common impulse for companies working with data for the first time is to spot the statistical pattern and then go looking for a problem that it solves. Instead, the approach should start with the problem and map the problem to the data. If the problem is shared across multiple organizational units, combine and analyze different data sets to discover context-rich insights.
If you don’t have a solid data strategy in place, start by asking questions: What would happen if we raise the price of the product by $1? When would be the best time to release a new feature? What were the quantifiable effects of changing a distributor? Some questions may be too complex to map out depending on how far along your company is in its data adoption.
Success in data relies on a cultural shift
All the data in the world doesn’t equal data-driven productivity. Just like any major technological jumps in history, big data comes with a societal dimension: It requires us to re-think the traditional top-down hierarchy of an organization, how we evaluate business performance, and what it means for our occupational roles and responsibilities. It requires early champions, leadership support, active participation, and a willingness to change in order to succeed.
Former Chief Scientist at Amazon Andreas Weigend lays it out simply with his nine rules for big data. Of the list, more than half are about adjusting the culture around how we view data:
- Collect everything
- Start with the problem, not with the data
- Share data to get data
- Base the equation of your business on customer-centric metrics
- Drop irrelevant constraints
- Embrace transparency
- Make it trivially easy for people to connect, contribute, and collaborate
- Let people do what people are good at, and computers do what computers are good at
- Thou shalt not blame technology for barriers of institutions and society
What are your biggest challenges around managing big data? What are your best practices? Let us know in the comments section below.
AI facsinates me, but I can't help to think at what cost? If technology is used as a tool to assist others and make things convient then great. But look what “convience” has done for us. I see kids glued to phones and computers, where now convience has become priority. I love the idea of AI, I can only hope that it doesn't take over my job doing web design.
I might agree with you that from the flipside of the coin, there are some upsetting effects of the technologies. People became dumber with the gadgets, as they do almost all the work in some industries.
The AI technologies are the next huge step in our development. But there still something that will not be simulated or replaced anyway, the things designed by the creativity. I'm sure that no AI will draw something as inspiring as Mona Lisa, or build Louvre, or write the “War and Peace”. As I work often for educational resources there are companies that provide some essay services, kind of work for freelancers offering their writings to the college students. And for sure in that business model, no AI will replace those guys.
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The AI will be something that it will not be easily stopped as seen from many factors world wide.
These Artificial intelligence is causing more and more people to lose their jobs for machines. Creators of these robots and business owners most benefit from IA. Other than that we will see a concentration of wealth on their hands and more people going jobless.
Of course there are some jobs that can't be replaced by IA but it's really a disturbing phenomena.
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let's hope it wont come to that else we will be losing our jobs. This world is going into the wrong direction with the AI.
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Whatever it would be desirable in reality but all children and adults are tied to their gadgets. This is a great victory and a crisis of our time. As for me, I like to order food, clothes and even inexpensive essays on the Internet. It saves my time and is very convenient. But it's awful when you walk with friends and they can not tear your eyes off the screens. Are you sitting in a big company, but are you alone, as is it possible?
Nice post, something really new about the modern technologies.
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Regardless of whether you are an individual or a business owner, data is important to you in so many different ways. There is so much that you can extract from just a single set of data alone. From finding out how much you have spent to knowing whether or not your customers are satisfied with your services, everything lies in a set of data.
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