Some technologies integrate data from different sources directly into a platform, skipping the need for additional data warehousing, while being able to deliver real-time interactive charts that are easy to interact with or to understand. These PaaS or DaaS solutions allow end-users to work with the data without requiring technical knowledge.
Learning from visualizations
There are big data technology vendors that focus on delivering the optimal graphical representation of big data. Visualizing unstructured and structured data is necessary to make the data understandable and turn it into information, but it is also very challenging. New big data startups however seem understand the practice of visualizing and have developed different solutions. One example is visualization based on the visual cortex of the human eye. This maximizes the ability of pattern recognition for the human brain. It makes it easy to read and understand massive amounts of relational data. The use of color and different thicknesses of the threats shown within the cortex allow users to easily recognize patterns and discover abnormalities.
Another way of visualizing is to use a technique called topological data analysis. This type of analysis focuses on the shape of complex data and is able to identify clusters and any statistical significance that is present. Data scientist can use this to reveal inherent patterns in those clusters. This type of analyses is best visualized with 3D clusters that show the topological spaces and can be explored interactively. Ayasdi is a big data startup that is capable of performing such visualizations.
It is definitely not always a necessity to have complex, innovative and interactive graphical representations. Infographics are visual representations of information, data or knowledge and they can help to make difficult and complex material quickly understandable. Dashboards combining different data streams showing ‘traditional’ graphs (column charts, line charts, pie charts or bar charts) can also provide valuable insights and there are many startups offering such solution.
Sometimes, real-time updated simple graphs showing the status of processes already provide more valuable information to improve decision-making then complex innovative visualizations. Visualizations on mobile devices get a completely new meaning when a user is able to play intuitively with the data while swiping, pinching, rotating or zooming on a mobile device.
Predicting the future
Having real-time analyses visualized in a great way is important, but being able to predict future outcomes will provide even more value to organizations. Analysing current and historical big data can help to make predictions about future events. This is a huge difference from existing business intelligence, which normally only looks at what has happened using analytical tools but this says nothing about the future. Predictive analysis can help companies provide actionable intelligence based on that same data. Prescriptive analytics is the next phase, which I discussed already before.
Also machine-learning platforms, such as Skytree, can predict trends, make recommendations and reveal untapped markets and customer based on available data. Machine learning goes much further then general business intelligence. Machine learning is about creating algorithms and systems that can learn from the data they process and analyse. The more data processed, the better the algorithm will become. Nowadays, Machine learning can be found in many applications, ranging from self-driving cars, to effective web search and speech recognition.
Customer Profiling
Profiling of (potential) customers is used to better target customers and better understand (potential) customers. The ultimate goal should be to develop a 360-degrees view of each individual customer, so that eventually an individual offering can be created. Behavioral analytics can be used to discover patterns in (un)structured data across customer touch points, giving organisations better insights in the different types of customers they have.
Sentiment analysis is used to understand how customers think about a company, brand or product. It uses natural language processing, text mining and data mining capabilities to find subjective information hidden in the data. This information refers to the attitude of the data, whether this is positive, negative or neutral. Proper sentiment analysis has to take into account the meaning of the words, the context in when someone said something through what channel. Blog posts, comments and tweets are generally analysed to determine the sentiment and using real-time analytics this will provide valuable information to an organisation. There are many big data startups that focus on this area and one well-known company, Bluefin Labs, is so good at this that Twitter acquired them earlier this year.
The profiles can also be used within recommendation systems. Of course we have the recommendations from large web shops such as Amazon.com that recommend other products that a user can buy when he or she is in the process of checkout. With big data, real-time recommendations are possible and also more extensive recommendations. Decide for example helps consumers with data-backed recommendations whether to buy now or wait for a new upcoming product.
It may be clear that there are so many different possibilities with big data. The global big data landscape with big data startups focusing on different areas is growing rapidly. Therefore we are developing the Big Data Strategy Model to provide some clarification and guidance for organisations in finding the right big data technology. This innovative, one-of-a-kind, model will be revealed soon and will be available for free. It will enable organisations to understand what they can achieve with the data they have, what data they need to develop a certain strategy, which big data technology they need to do that and what big data technology vendor could help them with achieving that strategy. Leave your email address here if you want to be notified when this model is launched.