Innovate or die – haven’t we all heard this? Add red tapism and innovation in the same sentence, sounds conflicting, right?
Organisations try to live up to “Innovate or die” by trying to become Agile or migrating to cloud services or adopting OKRs, basically moving in the right direction to be able to deliver better and faster. As technology advances exponentially, customers become more demaning, organisations must now be able to quickly respond to market demands in order to compete, which requires agility.
However, trying to be agile in an established, organization with legacy systems and non-agile methods is a big challenge. Untangling teams and systems that have always worked in silos is a big hurdle and the hardest part of any organisational transformation, not to forget the red tape created by outdated processes. Earlier, organisations created processes and procedures to ensure predictable outcomes, to mitigate risks. The processes designed did not have much room for experimentation or agility. But in the current digital landscape, this type of bureaucracy is simply too time consuming and not at all cost-effective.
- Most enterprises have standardised tedious approval processes where some of the people approving do not even possess the technical know how to judge or review the matter in question. This ends up in unending rounds of justifying the simplest decision.
- Procurement teams also add to the red tapism making it difficult for teams to acquire services and products that can speed up their development. We have all been through this – waiting months to get Slack or JIRA approved. And IT Security will not allow Trello or Google Drive, so go figure!
- And then there is the fear of cloud solutions. There is no denying privacy is a major concern when it comes to data and customers want to ensure that the services and products they use, handle their data well. But a cloud solution provider is more likely to have robust, well-configured firewalls and data security practices than an average enterprise, as it is the focus of their business. Keeping in mind that the cost of regulatory compliance will be substantial, but the cost of non-compliance will be higher, is important while choosing cloud service vendors.
- To top it all there is the fear of unknown, which is a huge blocker for innovation, it is therefore important to educate and get a buy-in from everyone involved on a transformation journey.
To be able to innovate, enterprises need to deliver end-to-end business value in increments, test and validate results before starting full scale development. Creating a culture of testing and experimentation demands processes and methodologies that support faster delivery of customer-centric value, with constant room for improvement.
Starting off testing a few assumptions that could lead to a potential minimum viable product should not require written approval from legal, compliance, finance, risk management, procurement, etc. Experimenation could be conducted with data masking and that should not entail long winded paperwork such as a detailed risk analysis and architectural artefacts.
Red tapism is a sure shot way to kill creativity which in turn ensures no innovation or improvement.
If organisations really want faster qualitative deliveries, freethinking leaders should not be afraid to rock the corporate boat and cut some slack in terms of obsolete processes and procedures.
I have not been regular with my personal blog because I have been blogging elsewhere.
Here are the links to my latest blog posts about why Big Data projects fail and how to attract more women into tech.
Having worked extensively in the Big data & IoT space I have closely observed failures over and over again and the reasons for failure being repetitive :
- Wrong use cases
- Wrongly staffed projects
- Obsolete technology
Read the blog post for more details:
Three reasons why Big Data projects so often fail
Being a woman in tech or woman in data I am often the only woman in meetings, trainings and discussions which feels weird. With not many women in tech it gets easier to discriminate the few that do exist. Incidents of mansplaining, gaslighting are rampant and it’s the victim that gets labelled as drama queen while the abusers fo scot free. Organisations that are serious about increasing the number of women in tech need to address glass ceiling, gender wage gaps & bro-culture and cultivate an inclusive work atmosphere. Read my post on how to get more women into tech.
How to Get More Women in Tech
Not a day goes by when our LinkedIn news feed is not flooded with the mentions of AI and Machine Learning benefitting and changing the ways of mankind, like never before. This hype surrounding AI, Machine learning has resulted in most organisations jumping on the bandwagon without proper evaluation. A couple of years ago, the term Big Data enjoyed a similar hyped status but it has been losing it’s lustre to all the talk about AI and Machine Learning, lately.
The truth, however, is that, AI and Big data need to coexist and converge. Merely collecting and storing data in huge amounts will prove futile, unless AI and Analytics are used to generate meaningful insights that help businesses, enhance customer experience or increase revenue influx.
Making an organisation Data-Driven will take time and will happen in stages. While there are no sure shot ways to create a Data-Driven organisation, below are some ways that could lead to a change:
- Strategy – It all starts with a clearly defined strategy in place, stating the Whys, Hows, Whos and Whens. A clear strategy helps in raising awareness across the organisation, about the topic in focus (data in this case) and creates a sense of urgency around the change process. It is imperative that the entire organisation understands the importance and implications of a data-driven organisation, thus encouraging people to update their skill sets and raise their level of data awareness. An all round data strategy should not only include the technology required for execution but the kind of competence and people skills and the sort of conducive atmosphere required for a data-driven organisation to thrive.
- People – Just as there are different kinds of skills required within a Marketing or a Software organisation, there are different skill sets for the different job roles within a data organisation. But due to the hype surrounding Machine Learning and AI while companies lack the practical knowledge in data know-how, the tendency is to either hire the wrong people or assign the wrong tasks to the right people! Not everyone has to be a data scientist in the data organisation. There will be people required to work on data architecture, data infrastructure, data engineering, data science and the Business Analysts. These could very well be the same person, if the organisation is lucky enough. But it is unfair to hire a data engineer and assign him/her the task of building Predictive models or hiring a data Scientist to be told to develop BI reports. Strategists will have to spend the time required to understand the nuances of skills and expertise required in a data organisation but it will be worth it, to retain and grown the talent pool required for a Data-driven organisation.
- Patience – Creating a Data-driven organisation will require ample amounts of patience and perseverance. If data has not been involved in the decision making process, earlier, then the data is most probably not in a state that can be used readily or maybe there is no or not enough data to begin with! In that case, it has to start with gathering the data required to achieve the business goals. Transaction systems have a very different database design than the data storage mechanisms used for Analytics purposes, which entails a design and architecting process before being able to analyse the data. Moreover, as Analysts dig into the transaction data, they surely will encounter non-existence of relevant data, data retrieval issues and unearth data quality issues and data integration problems due to the existence of data silos. In a data-driven organisation, all data sources are integrated to provide a single enterprise version of truth, irrespective of Customer data or Sales or Marketing data. A data platform, integrating all business data sources, ensuring quality and data integrity and security is a time-consuming process. Organisations will have to take this lead time into consideration when strategizing a Data-driven decision making approach.
- Organisational Culture – The purpose of a Data-driven organisation is to empower employees by means of data and information sharing to enable the organisation to collectively achieve the business goals. This approach requires employees to be data aware and not use gut feelings to make decisions and this could be a whole new approach for many. This new way of working requires organisational change management, educating people to use facts and figures to arrive at conclusions and make decisions. If an organisation is fairly data aware, in the sense that metrics are used to measure certain processes, in order to turn Data-driven , the organisation has to take steps to use data proactively (read Predictive Analytics) and not just summarise events that happened. The CDOs/ CMOs need to drive data awareness by showcasing quick wins and success cases of Data-driven approaches, as a means to use data as the foundation in every decision making process.
Some organisations may take longer to implement a Data-driven culture than others but there is no way an organisation can become Data-driven, just like that, one fine day! If the CDOs can gauge that the organisation has a longer incubation period then it is good to start with raising data awareness and introducing a BI/ Datawarehousing team. It is not recommended to directly leap on to AI, hiring data scientists, to be then left in a lurch if the organisation and the infrastructure are pretty rudimentary to handle their expertise.
A Data-driven organisation culture starts with the right strategy in place, followed by the right people and technology, evaluating and optimising the entire process, intermittently.
Every organization irrespective of industry has several business processes, each business process being supported by several IT products. Each of these IT products have an insurmountable amount of information that can generate insights which are paramount for any organization. Businesses that have been around for a while have obsolete processes and legacy systems that support the same. A typical organization independent of industry has transaction processing systems, CRM systems, ERP, billing and business analytics solutions. Each solution in itself is a silo if not integrated with the rest of the solutions. Granted that each of these solutions harbour valuable information but the the information residing in each system does not generate a holistic view of the business.
Integrating the silos is a Herculean task, or so it may seem, if the solutions are outdated and do not support APIs, plug-ins and adapters. Most CRM, ERP, Marketing automation products, lately are equiped with some form of connector, enabling data blending. If an organization has systems that do not support the above, then it is wise to migrate or upgrade the solutions to versions compatible with data extraction. Migrating legacy systems is a rocky road but the trade off being elimination of data silos. Often the implementation cycle of new software solutions are so long that the idea becomes outdated even before the roll out. Ofcourse there exist solutions with shorter time-to-market, for example data analytics platform that are run on Spark have a faster implementation cycle and are scalable, providing the flexibility that growing businesses need.
It was not long ago that marketing and data analytics borders got blurred due to new business needs. This has resulted in complex technological challenges. Not all businesses have the budget and resources to invest in migrating and upgrading most of the legacy systems. But in order to appease todays demanding customers, data integration is the key. No customer would like to remember or rummage through their homes to find old reciepts or mails when they call the customer care for a service or to complain. They would very much expect that on identifying themselves, the customer care representative not only solves their grievances but also comes up with suggestions to improve their customer lifecycle, which can be only attained by integrating data from disparate systems to gain a 360 degree view of the customer journey. Data integration, thus is a matter of being in business or out.
To start with, businesses should identify each data silo that exists and the function that each of them fulfill. (There maybe exist examples of one business process that is fulfilled by several software solutions. If an organization lacks data governance, then the number of redundant solutions and products can be plenty.) Listing and mapping business processes to softare solutions clarifies the current architecture. The next process is
- To identify the to-be roadmap
- Map solutions that support data blending, to each of the business process
The solutions that are adapted for new age businesses require to embody the following characteristics:
- Easy to implement
- Short implementation time
- Compatability with a wide range of disparate systems
- Easy to implement data security and access rights
- Forward compatible
Businesses need technology that support business gain and growth and the ever changing rules of the game (read disrutption).