#analytics#big data

Five Big Data Trends To Watch For In 2015

Teradata
|Dec 13|magazine12 min read

Used properly and analysed effectively, big data can give organisations customer and market insights that can be the difference between market leadership and business failure. Teradata has identified five key trends that are likely to emerge in 2015.

1.        Connection analytics 

Connection analytics understands the way people behave and how they are connected to each other. Businesses are increasingly interested in this information to further promote their products and services to target audiences. This insight can also highlight customer service issues. For example, people often converse with each other about their experiences with businesses on social media platforms. 

Although social network analysis isn’t new, interest in understanding consumer behaviour on these platforms is still constantly growing, particularly in understanding who consumers are conversing with. As an analytics problem, the complexity of connections grows exponentially and as a result the ability to process and interpret results at any scale has been hindered. With the explosion of mobile and digital data, this problem is further exacerbated but recent technological advances are quickly catching this wave. 

2.        The growth of the ‘discovery zone’

Advanced and sophisticated analytics lets organisations understand customers’ behaviour and how they interact with each other via platforms such as social media. But the speed of data access and analysis is critical: companies need to move fast to retain the competitive advantage that comes from having near real-time information. 

This need will see a continued growth in the trend towards creating a ‘discovery zone’, a data research and development hub of analytics. This is a dedicated area, preferably integrated with the data warehouse, where a number of data analysts can load and test new data and analytic models. Some organisations, such as Westpac, have seen significant success using this approach. 

3.        Improved access to big data

Accessibility to big data is still an issue for many organisations. Many companies have fallen into the trap of dumping data into systems that have either very basic access mechanisms that business users struggle to use, or there is an unnecessary handoff to IT to build the analytic model. It is critical for companies to have a business sponsor and a business driver for new analytics to ensure that accessibility is addressed. 

The iterative ‘sample, test and learn’ nature of analytic modelling exacerbates this issue. Currently many businesses are outsourcing rather than training internal staff to access and analyse data. This will see the rise of analytics consultancies. Organisations will also start to address the issue by focusing on tools and technologies that let users access the new functional capabilities of big data analytics.

4.        More efficient, automated data management and processing

Data lakes are large repositories of data that is in many different forms, including structured, unstructured, video, text and more. However, if a data lake is seen as a dumping ground for data and is not continuously managed, it can become a data swamp. 

As organisations come to grips with how to handle big data more effectively, they will look to better redesign and rebuild some data ingestion and integration tasks. This is likely to result in an increased uptake of data integration optimisation services to remove some of the unnecessary overhead and costs of data replication and processes. 

Organisations are also likely to gain a better understanding of the relative value of data, not just the cost and monetisation. This will let them make use of premium storage and processing capabilities such as in-memory computing. 

To gain further efficiencies, organisations will look beyond the one-size-fits-all approach to find data solutions capable of intelligently storing and processing data on multiple media within one platform, without the need for human intervention.

5.        The use of apps to gain customer insight

It’s widely acknowledged that the use of mobile technology is a continuing trend in IT. From an analytics perspective the use of mobile presents a novel and hitherto underutilised source of data for insight. Data is increasingly collected by organisations via mobile apps, which can track individual customers’ behaviours and purchasing patterns. Organisations can then rapidly use this analysis to understand if the App they deliver is providing an effective service and feed that back into the development process. 

This individual information isn’t freely available via the web as it’s hard to know who is really behind the computer.  The mobile trend will continue to gain momentum as businesses realise how valuable the customer data is that they can obtain from apps.