Image: SAP Cloud Platform

Hadoop is a Java-based, open source framework that supports companies in the storage and processing of massive data sets. Currently, many firms still struggle with interpreting Hadoop’s software and are doubtful about whether or not they can depend on it for delivering projects. Even so, it’s essential to understand just how much Hadoop enables businesses to do.  When it comes to analyzing large amounts of data at a low cost, it’s hard to do better. Before Hadoop emerged, businesses relied on expensive servers for their data analysis.  Now the process has become a lot more organized and much more efficient.

Hadoop functions by distributing gigantic data sets across hundreds of inexpensive servers that operate parallel to one another. It is also a cost-effective storage solution for businesses making use of data sets. Hadoop’s unique storage method is based on a distributed file system that basically ‘maps’ data wherever it is located in a cluster. When it comes to handling large data sets in a safe and cost-effective manner, Hadoop has the advantage over relational database management systems, and its value will continue to increase for businesses of all sizes as our world’s caches of unstructured data continue to increase. For this reason, leveraging Hadoop’s big data services is of growing importance to more organizations than ever before. This is why the International Institute for Analytics along with SAS has put forward 5 steps for maximizing the value of Hadoop big data services.

Formulating a Strategic Plan

First and foremost, focus on a target audience. The best way to do this is to examine the behavior of customers. The next thing to do is to select a particular data set that is not presently part of any other study in the enterprise data warehouse. The reason for conducting such a study is to obtain insights and feedback from the target audience about the brand and how effective your particular plan/service/commodity will be in the event that your business decides to test it out on the market.

An intelligent way to define and recognize the use cases is by using BAMA(SAS Business Analytic Modernization Assessment). Usually, this service helps in widening the use of analytics in the company and facilitates a smooth communication between the business units and IT.

Weighing the Benefits and Drawbacks of Hadoop

In the past, most companies have been dependent on analytics and business intelligence projects like data warehouses for storing their data. This is because there are times when a data warehouse is still a more reliable tool  (though Hadoop is still a much more cost-effective data storage option). Nevertheless, most industry veterans strongly believe that in the years ahead, Hadoopdoop will prove its worth by emerging as a formidable competitor.

Hadoop is not a good option for real-time processing of records that are small in number, but it is perfect for storing things like sensor data. Hadoop can be used to store sensor data as long as the collection of data from sensors is distributed across a large Hadoop cluster of commodity servers – all processing in parallel and ensuring very fast data-processing. For maximum efficiency, store large data types in Hadoop clusters. Then they can be passed on to an enterprise data warehouse whenever a production application is needed.

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Augmenting Hadoop for Delivering Value Results

After gaining a better understanding of your software and applying it to attain insights regarding your company’s specific needs, the next task is to begin manipulating and managing your data in a manner that continues to be relevant to your goals. While doing so, be sure to select tools that are capable of keeping pace with Hadoop.

Intelligently organizing the overall time to value will further acquaint you with the capabilities of Hadoop. How can this be done? First be sure to have reliable access to the data stored in Hadoop or elsewhere, whenever you need it. You can traverse millions of data rows in seconds and then work with data in Hadoop without the need to move it between different platforms.

Reassess the Need for Governance and Data Integration

The results of a data analysis project obtained here may be used for developing large-scale business strategies. Two major elements are governance and data integration. For these, it is essential to make sure that all the data that is gathered arrives from an authentic, clean source. The organization’s data governance practices must allow it to have the highest standards of confidence in their information sources, and be able to identify faults in the event of manipulations.

Consider Utilizing the Cloud

Instead of trying to figure out how much additional infrastructure you require for analyzing and processing your data, consider utilizing the cloud. Many cloud-based services like AWS(Amazon Web Services) provide subscription services like DynamoDB(a NoSQL Database) or Elastic MapReduce(EMR) for processing big data. AppEngine, the Google’s cloud application hosting service also provides a MapReduce tool.

Provide Self Service

It is critical to offer self-service access for business users. This will provide advantageous insights from data sets as you integrate more information into your business Intelligence framework. Offering built-in drag and drop fields in order to perform iterative and custom analysis is also a very useful way to streamline data analysis tasks and may also help you uncover previously hidden opportunities for creating value.  It’s also helpful when you are processing and storing data.

Assessing Gaps and Developing a Strategic Plan

Today, big data is only in its initial phase of development. Demand for the skills needed to handle a project of any size will continue to grow. In order to use Hadoop software productively, expertise is needed in programming languages such as Pig, Sqoop, MapReduce and Hive. Employ people who have these skills or provide sufficient training to your in-house team to become proficient in these programming languages. By following these as well as the other steps specified above, you can maximize the services of Hadoop and achieve the best possible results.

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An innovation that creates a new value network and market, and disrupts an existing market and value network by displacing the leading, highly established alliances, products and firms is known as Disruptive Innovation.  Clayton M. Christensen and his coworkers defined and analyzed this phenomenon in the year 1995. But, every revolutionary innovation is not disruptive. When a revolution creates a disorder in the current marketplace, then only it is considered as disruptive.

The term ‘disruptive innovation’ has been very popular over the past few years. In spite of many differences in application, many agree on the following.

Disruptive innovations are:

• More accessible (with respect to distribution or usability)
• Cheaper (from a customer perspective)
• And utilize a business model with structural cost advantages (with respect to existing solutions)

than their existing counterparts in the market.

The reason why the above characteristics of disruption are important is that when all 3 exist, it is very difficult for an existing business to stay in competition. Whether an organization is saddled with an outmoded distribution system, highly trained specialist employees or a fixed infrastructure, adapting quickly to new environments is challenging when one or all of those things become outdated. Writing off billions of dollars of investment, upsetting the distribution partners of your core business, firing hundreds of employees – these things are difficult for managers to examine, and with good reason.

Every day, new technologies emerge. The vendors in the market get shaken only if the technological innovation is extremely powerful. Big Data technologies such as NoSQL and Hadoop could be seen as catalysts for this type of innovation. We should understand here that big data is just raw data. The disruptive innovation coming from big data are big data analytics processes and technologies.

In the marketplace, big data is a disruptive force. It means that people require more than new skills, technologies and tools. They need an open mind to rethink about the processes they have followed for a long time and transform the way they operate. However, it is not particularly easy to force this type of change on long-time employees.

This must be viewed differently as many people believe that big data is a disruptive opportunity. Instead of the challenges that are stated above, we should consider 2 positive aspects:

• There is an opportunity to gain advantage from the flux occurring in the market, market changes and disruptions.
• Opportunity abhors a vacuum. If you don’t take advantage of the opportunities, you should expect that others will.

In his seminal work, The Innovator’s Dilemma, Clayton M. Christensen states a path forward for disruptive, new innovations in the following 4 steps: 

Phase 1 – Performance

There are various new market entrants at this stage with a large amount of chaos and the major focus of customers is on the emerging feature sets and functionality. When a technology arrives in the market, the first thing people look for is advanced features and high product performance, while ensuring it is doing the new thing they expect.

Phase 2 – Reliability

When the market reaches this stage, people have accepted the feature set and they now want reliability and stability in the products. There is a shift in focus from ‘does this product do what we expected’ to ‘how reliable is this product.’

Phase 3 – Convenience

Here, the relevance for big data implies making the software accessible on mobile devices in the form of iPhone apps or similar ones. Instead of making the software products that are command-line driven, the UIs that are appealing have become operative and the customers began demanding them.

Phase 4 – Price   

Once the above 3 phases have been completed, all market players have equal opportunity and they will start competing on price. When other criteria are satisfied and product turns into a commodity, the price will be the only differentiator.

With Big Data, I think we are still early in this lifecycle. Most of the products are in Phase 1, and some are entering Phase 2. If you consider Hadoop in spite of the amount of hype, few organizations are not using it. They want to utilize it as part of their Enterprise Data Warehouse, or as part of a Data Lake. Hadoop needs to have some features to make it more reliable for the enterprise for this to be a reality. It is getting there because active users of Hadoop are working on this, as are its vendors, like Pivotal and Cloudera. Expect a similar evolution for the types of tools along this continuum, and for Hadoop vendors and other somewhat highly established technologies of Big Data, as they begin to think of convenience and add reliability. YARN is an instance of emerging technology like Hadoop.

With information at the centre of most modern disruptions, there are new opportunities to attack industries from various angles. In a fragmented limo market, Uber built a platform that let it go into the broader logistics and transportation market. Through streaming video, Netflix grabbed users’r attention and it utilized the data it had to stir up the content production process. With a web mapping service known as Google Maps, Google mapped the world and then took its understanding of street layouts and traffic patterns to build autonomous cars.

There is not even a small doubt that disruption is in progress here. The products are created by these players and they are more accessible and cheaper when compared with their peers. It is coming from orthogonal industries with strong information synergy but not necessarily starting at the low end of the market. It is beginning where the source of data is and then building the information enabled system to attack an incumbent industry.

It is time for innovators, entrepreneurs and executives to stop arguing over whether something satisfies the traditional path of disruption. The disruption enabled by data may present an anomaly to the existing theory, but it is here and it is here to stay for a long time. The new questions must be

• How can you adapt in the face of this new type of competition?
• When data is a critical piece of any new disruption, what capabilities do you need and where do you get them?
• How do you assess new threats?

In order to succeed in this new environment, businesses require a thoughtful approach to recognize the potential threats combined with the will to make the right long-term investments — in spite of short-term profit incentives.

In spite of various wild predictions made regarding big data, the reality is that big data is disruptive and it must follow an established path. Businesses need to know in which disruption phase they exist and should make sure they are meeting the requirements of current phase as well as the next phase in the progression. This is extremely important to understand to define as well as implement a big data strategy successfully and meet the needs proactively.

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Big data sets are so complex and large that common data processing tools and technologies cannot cope with them. The process of inspection of such data and uncovering patterns is called big data analytics. The basic question which arises in our mind is, “In what way is the drug discovery related to big data analytics?” or “how is data analytics useful in the process of drug discovery?”

The process of drug discovery requires the analysis, collection and processing of unstructured and structured biomedical data which is of large volume from surveys and experiments gathered by pharmaceutical companies, laboratories, hospitals or even social media. These huge amounts of data may also include data regarding sequencing and gene expression, molecular data which is included in drug data, data consisting of drug and protein interaction, data of electronic patient record and clinical trial, self-reporting and patient behaviour data in social media, data of regulatory monitoring, and literatures where protein-protein interaction and drug repurposing and trends may be found.

To examine in detail such diversified types of data in huge volumes to be able to discover new drugs, we need to have algorithms that are scalable, efficient, effective and simple. We now discuss how recent innovations in big data analytics improve the process of drug discovery. Algorithms are developed to uncover the patterns which are hidden in such data as unreported, discussions on drug side-effects in social media communications, sequencing and patient record data, drug-protein interaction and regulatory monitoring data, data regarding chemical-protein interactions etc., for the prediction of drug side-effects and how these types of predictions can be used to identify the possible drug structures with different necessary features. Big data analytics also contributes to much better drug efficiency and safety for regulators and pharmaceutical companies.

Upon implementing several measures of big data which are technology-enabled, pharmaceutical companies can enlarge the data they gather and enhance their approach to analysing and managing this data.

1.Integration of all the data

One of the biggest challenges facing the R&D organizations of pharmaceutical companies is having well-linked, consistent and reliable data. Data is the foundation upon which the value-adding analytics are built. Integration of efficient end-to-end data establishes an authoritative source for all the bits and pieces of information and correctly links different data which cannot be compared regardless of the source. Smart algorithms which link clinical and laboratory data, for example, could create automatic reports that identify applications or compounds that are related and raise red flags related to efficacy or safety.

2.Internal and external collaboration

R&D in pharmaceutical organizations is a secretive activity which is conducted within the R&D department with little external and internal collaboration. Pharmaceutical companies can extend their data networks and knowledge by enhancing their collaboration with external partners. Whereas end-to-end integration improves connecting the elements of data, the main aim of this collaboration is to improve the connections among all the stakeholders in delivery, commercialization, drug research and development.

3.Make use of IT-enabled portfolio for data-driven decision making

To make sure the allocation of scarce R&D funds is appropriate, it is critical to speed up decision making for pipeline and portfolio progression. Pharmaceutical organizations find it really challenging to make accurate decisions to about which assets to retain and which ones to kill. The financial or personnel investments they have made already may affect the decisions at the expense of merit and they lack decision-support tools which are appropriate to facilitate making calls which are tough. IT-enabled portfolio management enables the decisions which are data-driven to be made seamlessly and quickly. Smart visual dashboards must be used whenever there is a possibility to facilitate effective and rapid decision making.

3.Influence the new discovery technologies

Pharmaceutical R&D must continue using cutting-edge tools. These include systems biology and technologies that produce huge data very quickly. One of the examples for the technologies that produce huge data quickly is next-generation sequencing. This technology will make it possible to sequence an entire human genome within 18 to 24 months and at a cost of $100. The improved analytical techniques and wealth of new data will intensify the innovations of the future and feed the pipeline of drug development.

4.Deployment of devices and sensors

The advancement of instrumentation using miniaturized bio-sensors and the evolution of the latest smartphones and their applications are resulting in increasingly sophisticated health-measurement devices. Pharmaceutical companies are using smart devices to gather huge real-world data which was not available previously to scientists. Monitoring of patients remotely through devices and sensors constitutes an immense opportunity. This type of data can be used to analyse drug efficiency, facilitate R&D, create economic models which are new combining the provision of drugs and services and enhance future drug sales.

5.Raise the efficiency of clinical trials

A combination of smarter, new devices and exchange of fluid data will enable improvements in design of clinical trial and outcomes as well as higher efficiency. Clinical trials will become much highly adaptable to respond to drug-safety signals which are seen only in small but subpopulations of patients which are identifiable.

The following are the challenges facing transformation of bigdata in pharmaceutical R&D

1.Organization

The silos in an organization result in data silos. Functions usually have responsibility for their data and the systems they contain. Adopting a data-centric views, with a clear owner for each type of data through the data-life cycle and across functional silos, will greatly enhance the ability to share and use data.

2.Analytics and Technology

Pharmaceutical companies are following legacy systems containing disparate and heterogeneous data. These legacy systems have become a burden for these companies. Enhancing the efficiency to share data needs connecting and rationalizing these systems. There is also a scarcity of human resources supplied with a specific task of improving the analytics and technology needed to extract maximum value from existing data.

3.Mindsets

Many pharmaceutical organizations believe that unless they find a future state which is ideal, there is very less value to investing in enhancing the analytical capabilities of big data. Pharmaceutical organizations should gain knowledge from smaller, more entrepreneurial enterprises that see a lot of worth in the incremental improvements that get emerged from small-scale pilots.

Using Big data in pharmaceutical companies could slowly turn the tide of diminishing success rates and sluggish pipelines.

Conclusion

Effective utilization of big data opportunities can help pharmaceutical organizations better determine new ways to develop approved and effective medicines more quickly.

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