The marketplace and emerging trends in big data analytics
Data and analytics continues to add value to organizations by examining how decisions are made. Next generation analytics can automate the way decisions are made in areas like making inventory management, financial risk management, and sensor data management.
We now have greater accessibility to data. There is enhanced visibility of relevant information and massive amounts of data are now easily accessible. This results in improved reporting to clients and stakeholders. We can use retail, social and clinical data to precisely customize offerings and marketing campaigns to consumers.
We should think about not just business to consumer, but also business to business. Think about how the ability to use employee data to search for and discover new needs can enable organizations to fine tune optimal performance and efficiency. By using data and analytics to identify emerging trends, organizations will be able to create new service offerings for their consumers. And new business models who execute them.
Using data and analytics isn’t limited to only high tech industries. Whatever sector you operate in, be it finance, healthcare, retail, or just about anything else, data and analytics can and will play a critical role. Some examples include: discovering patterns in large numbers of service requests for a large bank; discovering which brands are most similar based on social media platforms and followers on each; gaining insight into an annuity policyholder behaviour based upon terabytes of consumer data.
Now, when one observes or collects data first-hand, this is called primary data. Primary data is very valuable in getting consumer behavioural insights.
If asking a consumer what kind of coffee they like or what kind they intend to purchase, we don’t learn what they’ve actually purchased. That’s where secondary data comes in.
Secondary data is data that is collected by someone or something else. In this case, one could collect the data from someone’s credit card statement and determine whether or not they actually bought the coffee.
Both primary and secondary data are used in building strategic decisions. Big marketing decisions and M and A decisions and beyond.
Right now, rapidly evolving technology and the availability of real time data is allowing organizations to use data analytics as the true game changer and the table stakes of the future.
Organizations are collecting massive amounts of data about their customers, and the they are expecting that this data is used intelligently and precisely.
If an organization is going to be an industry leader or an innovator, analytics is key to understanding correlation and causation in turning strategy into reality too. Organizations today are facing information overload as data increasingly comes at them in new formats, and at faster speeds and higher volumes than ever before. They have to use a combination of techniques, including simulation and visualization to manage, manipulate and organize this torrent of data. So they can address the progressively complex decisions they have to make from operational control, to management control, to strategic planning.
What does the organizational structure need to look like?What does it mean? It’s not just the data and analytics.
If you can’t get the data through the organization efficiently, it’s just a statistical model or it’s just an answer. Our clients are challenging us on a daily basis to help them figure out what an organization that fully uses the data and analytics must look like. What do the models look like?
Another vital source of data is social media. Generally, social media is defined as a set of internet technologies that allow users to generate and share content, collaborate, exchange ideas and form community with shared or opposing interests. Social media can be good or bad. So many clients think there is so much to find, but there is a lot of noise in social media.
So how do you extract that signal from the noise?
It’s important to look at data collected from social media platforms, along with all other relevant datasets. Social media is a powerful platform but it’s one that many organizations are chasing, and don’t have a sound social media or overall integrated data strategy. When you think about data and analytics, you need to think about the social media strategy in the organization.
What can someone bring from a social perspective?
Being able to challenge how social media is being used, and if it’s going to be used from an analytics perspective. Whatever social media data an organization is trying to use, it’s very important that you tie it to other relevant data sets to validate findings and ensure the accuracy of what you are projecting.
When used properly, data and analytics can change how organizations operate, allowing them to become more efficient and have greater impact on their bottom line and most importantly, their customers.
Now let’s look at how data and analytics can be used in the retail industry. Companies can use following information to optimize product mixes and make decisions related to product development:
1. Consumer purchase data,
2. Social networks, and geolocation information
For example, a retailer can use data generated from consumer mobile devices for location based sales and offerings. Take a local drug store where a customer has their mobile app installed on their phone. The drugstore can push a notification if you are nearby because it uses your geolocation information efficiently. Organizations can also use data from mobile devices for payments and purchase patterns. The organization can go well beyond just pushing offers, but using the data and analytics to optimize products and segments is one of the core components that underlies social media and data analytics.
The operations department at the same retailer can evaluate store traffic patterns to improve product placement and point of sales processes. And finally, the service department can evaluate online and real time interactions to improve and tailor in-store and post-sale experiences.
Another example of data analytics in action is from the healthcare industry. In terms of product development, analysis of disease trends in clinical studies can direct, and optimize research and development efforts for drug discovery. Data can help bring:
1. New drugs to market more quickly, and more efficiently,
2. Using things like claims data to more efficiently communicate with policyholders on how they can manage their health and prevent specific disease or illnesses from happening, diabetes or the common cold.
3. Analysis of patient history and disease patterns allows sales and marketing teams to target patients with care or lifestyle improvements.
4. By using claims data and patient demographics, hospitals can improve operations by optimizing treatment selections.
5. And in terms of customer service, health care providers can improve treatments and manage chronic illnesses through data collected from ongoing patient monitoring.
As the processing capacity of data analytics continues to increase, it opens the door to a broad range of advanced algorithms and modelling techniques that organizations can employ to draw a valuable insights from data and analytics.
There are different ways to model structured and unstructured data. Of course there are the traditional methods, but we also see many new modelling techniques emerging to handle all the different types of data that is available. For the analytics individual or for the advanced data scientist, there are a portfolio of methods that can be used today, more so than ever, given the advancements in processing power, mathematics, data storage and most importantly visualization.
In summary, what has fundamentally changed are having following three critical things:
1. the right people,
2. the right tools, and
3. the right algorithms in place to take advantage of the massive amounts of data that organizations have access to today.
