Synapse

Seven Data and Analytics Trends That Will Impact FinTech

As the customer experience takes center stage in the CEO 2016 agenda, businesses are increasingly turning to data and analytics to gain insights into rapidly changing customer expectations.

Indeed, according to Forrester’s “Top 10 Critical Success factors in 2016” report, analytics is a key competitive weapon for organizations to thrive in the age of the customer. In fact, Forrester predicts cutting-edge algorithms will give leaders a leg up over competitors drowning in data and using run-of-the-mill analytical tools.

Gartner goes even further with its “Top Strategic Predictions for 2016 and Beyond” paper to proclaim that by 2018, 20 percent of all business content will be authored by machines. Even more startling is their prediction that by 2018, more than three million workers globally will be supervised by a “robo-boss.”

Based on the convergence of analyst predictions, data and analytics are key enablers for business success. So what are the key technologies and trends in analytics that will impact the financial services industry the rest of this year and moving forward? The list will likely include the following:

  1. Hadoop will become mainstream as commercial vendors aggressively plug the gaps for production use (data security, governance, etc.). Constructs like data lake will gain adoption. We can expect significant growth in vendor tooling to manage and govern data stored in data lakes. Products like Apache Sentry—a system for enforcing fine-grained role-based authorization to data and metadata stored on a Hadoop cluster—will gain momentum. Extract, Hadoop and load (EHL) approach will become popular over extract transmit and load (ETL) / extract, load and transform (ELT) with traditional data warehousing. Traditional enterprise data warehouse (EDW) vendors like Sybase IQ (SAP), Teradata, etc., have already created connectors for Hadoop and popularized hybrid architectures, leveraging Hadoop for emerging use cases. Concepts like data virtualization (data federation and query federation) will gain traction to combine data from disparate sources, including Hadoop for business analytics.
  2. Apache Spark is already the most popular open source project in the Hadoop ecosystem and it will continue to see increasing adoption for real-time analytics, including stream processing and machine learning for timely business insights. It is also emerging as a popular choice over Hadoop’s original MapReduce framework for general purpose big data processing for improved performance and high-level developer abstractions.
  3. Next-generation EDW technology will focus on real-time intelligence. High-performance in-memory databases like memsql are seeing increased adoption. Capital market use cases include processing and streaming market data, real-time trade surveillance, risk and compliance management.
  4. Cloud-based analytics will gain wider adoption. AWS and Azure will continue to lead and mature their PaaS offerings, while new DBaaS offerings like Snowflake and IBM Cloudant will gain acceptance. Hybrid cloud will enable organizations to augment existing on-premise data capabilities with cloud offerings.
  5. 2015 was a breakthrough year for data science as predictive analytics powered by machine learning has finally taken off. In 2015, both AWS and Azure launched their machine learning platforms. Adoption will continue to grow as existing practitioners gain more experience, the profession attracts more entrants and vendors release new tools. However, the availability of quality data to feed machine learning algorithms will remain a challenge as organizations continue to struggle to bridge data silos across functions.
  6. Venture capital funding will continue for artificial intelligence (AI) startups. Deep learning will gain adoption in image recognition, language understanding, etc., exceeding human performance in many areas. AI promises to bring in operational efficiencies through automation of manually intensive functions. Last month, AlphaGo, the AI-based computer created by Google DeepMind, beat world champion Go player Lee Sedol—the first time ever that a computer program has defeated a human professional player. Startups to watch include Sentient technologiesVicarious and many others.
  7. The Internet of things (IoT) space is heating up. Gartner has predicted that in 2016 spending on new IoT hardware will exceed $2.5 million a minute! IoT will drive the growth of platforms focusing on real-time analytics capabilities. Cloud vendors like AWS, Azure and others have already come up with targeted cloud offerings for IoT. Hadoop vendors like MapR and Cloudera are also in the race.

Author

Saurabh Banerjee – Senior Specialist
Saurabh Banerjee is a Senior Specialist with Sapient. He has 20 years of industry experience spanning technology consulting and product development. In his current role, he is responsible for developing data analytics capabilities and innovation efforts at Sapient Global Markets. He is passionate about emerging technologies and its impact on businesses, economy and our daily lives. You can follow Saurabh on Twitter @saurabhbanerjee.