9TECH OUTLOOKJanuary 2017would have been happy with standard reports are now more advanced. They look for self-service data exploration and visualization capabilities in order to consume and interpret the variety of data that supports their individual interests in the timeframe they need it. Here are 10 tips Life Sciences companies can follow to succeed in the world of data analytics even as the data warehouse fades away. 1) Get out of your comfort zone and be innovative! Given the current pace of change, everything we do with data should be looked at critically. Create benchmarks to trigger change. Data driven projects that will take more than 6 months to deliver business value or that come at a significant cost should trigger a thought to consider alternatives. 2) Take data directly from the source systems when possible. Modern technologies will insulate the source systems from over use and facilitate high performance results. This allows you to quickly create applications that meet rapidly evolving business needs while significantly reducing data management costs.3) Focus on centralizing master data rather than all data. If you are able to keep your master data current and accurate, it makes it much easier to bring together the various sources of data available to you. This will also force you to think more carefully about what data you should, and what data you should not, storeand maintain.4) Move from technical to business centric application design. Rather than trying to store all data to address every possible business need, start with a specific business use case and find the data that supports that need. Look beyond your own organization and identify information that will add unique value for your business users. 5) Bring data consolidation closer to the business user. Involve someone from the business in the entire process from data sourcing through to consumption. The market is filled with technologies that empower the end users to collect, integrate, cleanse, and consume data for purposes of analytics. 6) Adopt a "move fast" mentality. In order to take advantage of opportunities, your business has to make decisions quickly. Giving them something good now is far more valuable than givingthem something perfect six months from now. Decide which data will add the most value and start with that. Expand incrementally in rapid fashion; plan to deliver in weeks not months. 7) Treat data quality as an on-going effort that provides continuous improvement but does not act as a barrier to providing information to the business users. Communicate and be clear about the state of the information and let the business decide how useful it is. Fix data at the source and be sure to involve business users in the data quality effort. 8) Create consistency through process and behavioral change. Don't let the idea of multiple applications or replication of data paralyze you. A "single version of the truth" is still very much alive, despite the fact that data is coming from multiple places and then is used in different ways by different groups.Create consistency without losing your focus on agility by creating lite-touch process with defined standards and approved, common data sources, reports, connectors, and visualization objects. 9) Empower individuals to be self-sufficient and create a culture of sharing. Business users have unique needs but can gain efficiencies and be more productive by sharing information. There are various levels and forms of self-service. It is not helpful to insulate users; get started and move them along the maturity curve. 10) Consider full or hybrid cloud-based solutions for data infrastructure or analytics in order to reduce your dependency on internal skills and improve your ability to shift from one technology to another. With the growth of the cloud and collaboration with strategic partners, data is now coming from many places that extend far beyond our company's four wallsChristopher Ferrara
< Page 8 | Page 10 >